Economic Bulletin
Issue 3 / 2017
Contents
Update on economic and monetary developments
2
Summary
2
1
External environment
4
2
Financial developments
8
3
Economic activity
11
4
Prices and costs
15
5
Money and credit
17
Boxes
1
21
The ECB’s asset purchase programme and TARGET balances:
monetary policy implementation and beyond
21
2
Recent developments in youth unemployment
27
3
Assessing labour market slack
31
4
What can recent developments in producer prices tell us about pipeline
pressures?
36
The targeted longer-term refinancing operations: an overview of the
take-up and their impact on bank intermediation
42
5
Articles
47
1
The slowdown in euro area productivity in a global context
47
2
Harmonised statistics on payment services in the Single Euro
Payments Area
68
Statistics
ECB Economic Bulletin, Issue 3 / 2017
S1
1
Update on economic and monetary
developments
Summary
The ECB’s monetary policy measures have continued to preserve the very
favourable financing conditions that are necessary to secure a sustained
convergence of inflation rates towards levels below, but close to, 2% over the
medium term. Incoming data since the Governing Council’s meeting in early March
confirm that the cyclical recovery of the euro area economy is becoming increasingly
solid and that downside risks have further diminished. 1 At the same time, underlying
inflation pressures continue to remain subdued and have yet to show a convincing
upward trend. Moreover, the ongoing volatility in headline inflation underlines the
need to look through transient developments in HICP inflation, which have no
implication for the medium-term outlook for price stability.
Available indicators point to sustained global growth at the beginning of 2017, while
the recovery in international trade has continued. The global recovery is broadening,
with the improvement in growth being widespread across countries. International
financial conditions have remained overall supportive, despite significant policy
uncertainty. Global headline inflation has increased further, mainly driven by energy
prices. However, oil prices have recently undergone some volatility.
Euro area financing conditions remain very favourable. Comparing developments
between the Governing Council meetings of 9 March and 27 April, bond, equity and
foreign exchange markets overall show only small movements.
Incoming data, notably survey results, suggest that the ongoing economic expansion
will continue to firm and broaden. The pass-through of the monetary policy measures
is supporting domestic demand and facilitates the ongoing deleveraging process.
The recovery in investment continues to benefit from very favourable financing
conditions and improvements in corporate profitability. Employment gains, which are
also benefiting from past labour market reforms, are supporting real disposable
income and private consumption. Moreover, the signs of a stronger global recovery
and increasing global trade suggest that foreign demand should increasingly add to
the overall resilience of the economic expansion in the euro area. However,
economic growth continues to be dampened by a sluggish pace of implementation of
structural reforms, in particular in product markets, and by remaining balance sheet
adjustment needs in a number of sectors. The risks surrounding the euro area
growth outlook, while moving towards a more balanced configuration, are still tilted to
the downside and relate predominantly to global factors.
1
Taking into account information available at the time of the Governing Council meeting of 27 April 2017.
ECB Economic Bulletin, Issue 3 / 2017 – Update on economic and monetary developments
Summary
2
Inflation has been recovering from the very low levels seen in 2016, largely owing to
higher energy price increases. After reaching 2.0% in February, euro area annual
HICP inflation declined to 1.5% in March 2017. Measures of underlying inflation,
however, have remained low and are expected to show only a gradually rising trend
over the medium term, supported by the monetary policy measures, the expected
continuing economic recovery and the corresponding gradual absorption of slack.
Broad money growth remained robust, while the recovery in loan growth to the
private sector observed since the beginning of 2014 is proceeding. The euro area
bank lending survey for the first quarter of 2017 indicates that net loan demand has
increased and bank lending conditions have eased further across all loan categories.
The pass-through of the monetary policy measures put in place since June 2014
thus continues to significantly support borrowing conditions for firms and households
and credit flows across the euro area. Moreover, financing costs for euro area nonfinancial corporations are estimated to have remained favourable in the early months
of 2017.
At its meeting on 27 April 2017, based on the regular economic and monetary
analyses, the Governing Council decided to keep the key ECB interest rates
unchanged. The Governing Council continues to expect the key ECB interest rates
to remain at present or lower levels for an extended period of time, and well past the
horizon of the net asset purchases. Regarding non-standard monetary policy
measures, the Governing Council confirmed that the net asset purchases, at the new
monthly pace of €60 billion, are intended to run until the end of December 2017, or
beyond, if necessary, and in any case until the Governing Council sees a sustained
adjustment in the path of inflation consistent with its inflation aim. The net purchases
will be made alongside reinvestments of the principal payments from maturing
securities purchased under the asset purchase programme.
Looking ahead, the Governing Council confirmed that a very substantial degree of
monetary accommodation is needed for euro area inflation pressures to build up and
support headline inflation in the medium term. If the outlook becomes less
favourable, or if financial conditions become inconsistent with further progress
towards a sustained adjustment in the path of inflation, the Governing Council stands
ready to increase the asset purchase programme in terms of size and/or duration.
ECB Economic Bulletin, Issue 3 / 2017 – Update on economic and monetary developments
Summary
3
1
External environment
Surveys point to sustained global growth in the first quarter of 2017. The global
composite output Purchasing Managers’ Index (PMI) excluding the euro area
increased in March (see Chart 1), driven by a rise in the services index while the
manufacturing PMI remained broadly unchanged at three-year highs. In quarterly
terms, the PMI remained at about the same level in the first quarter of 2017 relative
to the previous quarter, pointing to ongoing robust growth. Quarterly PMIs weakened
in the United Kingdom and to a lesser extent in the United States, but picked up in
Japan. Among emerging market economies (EMEs), quarterly PMIs decreased in
China, but improved in Russia, India and Brazil – albeit remaining below the
expansionary level.
Chart 1
Global composite output PMI
(diffusion index)
global excluding euro area
global excluding euro area – long-term average
advanced economies excluding euro area
EMEs
60
58
56
54
52
50
48
2011
2012
2013
2014
2015
2016
2017
Sources: Markit and ECB calculations.
Note: The latest observations are for March 2017.
The recovery is broadening, with the improvement in growth being widespread
across countries. Indeed, the dispersion of quarterly growth rates across countries
has narrowed considerably in recent quarters. In particular, activity in commodity
exporters has stabilised following the rebound in commodity prices, while temporary
downturns caused by domestic factors in countries such as Turkey are also
bottoming out.
Global financial conditions remain broadly supportive. Equity markets have
moderated recently as investors had some concerns about the ability of the new US
administration to follow through on policy pronouncements. Yet despite significant
policy uncertainty, financial markets have been generally resilient, with risk aversion
low. The Federal Reserve System increased its official interest rates at its March
meeting. While other major central banks are expected to maintain an
accommodative stance, markets have also been buoyed by expectations that
monetary tightening in the United States will be gradual. In China, financial
conditions have tightened for banks and bond yields have increased, but benchmark
ECB Economic Bulletin, Issue 3 / 2017 – Update on economic and monetary developments
External environment
4
bank lending rates have remained unchanged. Financial conditions in most other
EMEs have improved with financial markets rebounding and, following some weeks
of outflows, capital has flowed back towards EMEs.
The recovery in global trade continued at the start of the year. Growth in global
goods imports increased to 2.8% (in three-month-on-three-month terms) in February,
the strongest figure in more than ten years (see Chart 2). The rise in momentum was
mainly driven by EMEs, with particularly strong improvements in central and eastern
Europe and Latin America. Leading indicators also confirm the positive trend. The
global PMI for new export orders increased to 52.5 in the first quarter of 2017,
pointing to a sustained recovery in global trade growth.
Chart 2
Global trade and surveys
(in three-month-on-three-month percentage (left-hand scale); diffusion index (right-hand scale))
global merchandise imports (left-hand scale)
global PMI new export orders (right-hand scale)
global manufacturing PMI (right-hand scale)
3
56
2
54
1
52
0
50
-1
48
-2
46
2011
2012
2013
2014
2015
2016
2017
Sources: Markit, CPB Netherlands Bureau for Economic Policy Analysis and ECB staff calculation.
Note: The latest observations are for February 2017 for global merchandise imports and March 2017 for PMIs.
Global inflation increased further in February, mainly driven by energy prices.
Annual consumer price inflation in the countries of the Organisation for Economic
Co-operation and Development (OECD) reached 2.5% in February, a level not seen
in almost five years. Excluding food and energy, OECD annual inflation remained
unchanged at 1.9% compared with figures for January. Slowly diminishing spare
capacity at the global level is expected to give some support to underlying inflation
looking forward, while the current oil futures curve anticipates very stable oil prices,
pointing to a very limited contribution from energy prices to inflation.
Since late last year when members of the Organization of the Petroleum
Exporting Countries (OPEC) and 11 non-OPEC producer countries agreed to
cut oil production, Brent crude oil prices have fluctuated in the range of
USD 49 to USD 56 per barrel. While global oil production dropped in January as
expected, in February oil supply in both OPEC and non-OPEC countries increased,
raising concerns about whether the supply curtailment would be complied with. At
the same time rising US crude oil inventories and shale oil supply further weighed
negatively on oil prices, sending them back to USD 50 per barrel where they had
stood at the end of November 2016. Since the beginning of April, prices have
ECB Economic Bulletin, Issue 3 / 2017 – Update on economic and monetary developments
External environment
5
reverted to a mild positive trend owing to a new decline in US inventories and
outages in Libya’s largest oilfield due to renewed geopolitical tensions. Expectations
that the OPEC cut would be extended for the second half of 2017 have also recently
been priced in. Non-oil commodity prices have decreased by around 5%, in US
dollar terms, since early March. This has been driven largely by a substantial decline
in the price of iron ore, due to high stocks at Chinese ports and expectations of a
moderation in Chinese steel demand and, to a lesser extent, due to a decline in food
prices. Other non-ferrous metal prices remained broadly stable.
The outlook for economic activity in the United States remains broadly robust.
Real GDP expanded at an annualised rate of 2.1% in the fourth quarter of 2016,
supported primarily by consumer spending and private investment. Survey and hard
data have diverged at the start of 2017, with consumer and business sentiment
continuing to be robust, while industrial production, core capital goods orders and
consumer spending are softening. However, some of the factors holding back
consumption are temporary, including exceptionally warm weather weighing on
energy consumption, and delays in tax refunds. At the same time, labour market
conditions continued tightening in March, with the unemployment rate reaching 4.5%
(below the Federal Open Market Committee’s estimate of full employment) and
annual growth in average hourly earnings at 2.7%. In March annual headline
consumer price index (CPI) inflation in the United States decreased to 2.4%, mostly
stemming from a decline in the energy component. The main components of core
inflation also moderated, leading to a fall in CPI excluding food and energy to 2.0%.
Economic growth in Japan remains modest. Real GDP increased by 0.3%
quarter on quarter in the fourth quarter of 2016, with both domestic demand growth
and net trade remaining subdued. After some weakness in January, industrial
production and real exports have rebounded and remain on average above last
year’s levels for the same period. Moreover data on private consumption point to
some tentative signs of a recovery, supported by developments in the labour market.
However, the tightening in the labour market, with the unemployment rate at its
lowest level since 1994, has not led to an acceleration in wage growth. Headline CPI
inflation increased to 0.4% in January year on year. At the same time, annual growth
in CPI excluding fresh food and energy – the Bank of Japan’s preferred measure of
core inflation – also strengthened somewhat, to 0.2%.
Following robust growth in the UK economy last year, recent indicators point
to a softer start into 2017. In the final quarter of 2016, real GDP increased by 0.7%
quarter on quarter. However, recent indicators overall suggest that the pace of
economic expansion softened at the start of this year. In particular, there are signs
that rising inflation is curtailing real incomes and private consumption. The pick-up in
inflation over recent months has been driven largely by energy prices and the
depreciation of the pound sterling since the UK referendum on EU membership. In
March 2017 annual CPI inflation stood at 2.3%. On 29 March 2017 the UK
government gave formal notice of its intention to withdraw from the European Union,
paving the way for EU-UK negotiations in accordance with Article 50 of the Treaties.
Economic growth in the Chinese economy stabilised. Real GDP grew at 6.9%, in
year-on-year terms, in the first quarter of 2017, slightly higher than in the previous
ECB Economic Bulletin, Issue 3 / 2017 – Update on economic and monetary developments
External environment
6
quarter. Growth was mainly driven by consumption, while the contribution of gross
fixed capital formation was the lowest since early 2015. However, overall momentum
in the first quarter was weaker than in the last quarter of 2016. It was also weaker
than what some available indicators suggested, in particular for investment and
construction, which could reflect a residual seasonality affecting the estimate for the
first quarter. Annual CPI inflation fell to 0.8% in February, from 2.5% in January, as
food and tourism services prices fell after the Chinese New Year holiday. Inflation
excluding food and energy decreased to 1.8% from 2.2%. Meanwhile, annual
producer price inflation rose to 7.8%, which is attributed to rising ferrous metal and
energy prices. Reductions in overcapacity in heavy industry have pushed up raw
material prices but this is likely to be temporary.
ECB Economic Bulletin, Issue 3 / 2017 – Update on economic and monetary developments
External environment
7
2
Financial developments
Overall, euro area government bond yields have slightly declined since early
March. The slight decline during the period under review (9 March to 26 April 2017)
has taken place in the context of heightened political uncertainty surrounding the
French presidential elections. As a result, a phase of declining yields between late
March and the day before the first-round results of the French elections were known
was partially offset by rising yields in the aftermath of the vote. Overall, the euro area
ten-year overnight index swap (OIS) yield declined by 5 basis points while sovereign
bond yields decreased on average by around 15 basis points. Across countries, the
declines ranged from a few basis points to around 70 points, while some marginal
increases were observed in Italy and the Netherlands. Spreads vis-à-vis the rate on
German ten-year bonds overall remained unchanged or decreased slightly in most
countries, with the exception of Greece and Portugal (where the declines reached
around 70 basis points) and Italy and the Netherlands (where spreads rose
marginally).
Chart 3
Selected euro area and US equity price indices
(1 January 2016 = 100)
euro area financials
euro area non-financials
US financials
US non-financials
130
120
110
100
90
80
70
01/16
04/16
07/16
10/16
01/17
04/17
Source: Thomson Reuters.
Notes: Daily data. The black vertical line refers to the start of the review period (9 March 2017). The latest observation is for 26 April
2017.
Euro area equity prices have increased since early March. At the end of the
period under review the equity prices of euro area non-financial corporations (NFCs)
were around 5% higher than at the beginning, while prices rose by almost 7% for
financial corporations. Overall, the recent positive developments in the euro area
stock market have led equity prices of banks to now stand around 70% higher than
the lows recorded in the aftermath of the United Kingdom’s referendum on EU
membership in June 2016 (see Chart 3). As has been the case for bonds, political
uncertainty has also affected developments in the euro area equity market: euro
area equities mostly moved sideways ahead of the outcome of the French
presidential elections only to then rise significantly. Since early March equity prices
of NFCs in the United States and the United Kingdom have risen significantly less
ECB Economic Bulletin, Issue 3 / 2017 – Update on economic and monetary developments
Financial developments
8
than in the euro area, while they declined marginally in Japan. The equity prices of
financial corporations underperformed relative to NFCs in all three economic areas.
Market expectations of equity price volatility increased significantly in the euro area
to around 23% ahead of the French elections, but reverted to the levels prevailing in
early March, i.e. around 14%, in the aftermath. In the United States, by contrast,
market-based expectations of equity price volatility remained broadly stable from
early March.
Spreads on bonds issued by NFCs declined marginally during the period
under review. On 26 April, investment grade NFC bond spreads (on average for
rating classes AAA, AA, A and BBB) were 4 basis points lower than in early March
and still around 25 basis points lower than in March 2016, when the Governing
Council announced the launch of the corporate sector purchase programme (CSPP).
Spreads on non-investment grade NFC and financial sector debt (which is ineligible
for purchase under the CSPP) also declined over the same period, by 10 and 4 basis
points respectively.
In foreign exchange markets, the euro recorded a small depreciation in
trade-weighted terms. In bilateral terms, from 9 March, the euro appreciated by
3.2% against the US dollar and by 2.9% against the Chinese renminbi. Such
developments were more than offset by a weakening of the euro vis-à-vis the
currencies of other principal trading partners of the euro area. In particular, the euro
depreciated against the pound sterling (by 2%) and against the currencies of most
other non-euro area EU Member States. In the case of the Czech koruna, the euro
weakened against it slightly (by 0.3%) following the discontinuation of the koruna’s
exchange rate floor (see Chart 4).
Chart 4
Changes in the exchange rate of the euro vis-à-vis selected currencies
(percentages)
since 9 March 2017
since 26 April 2016
EER-38
Chinese renminbi
US dollar
Pound sterling
Swiss franc
Japanese yen
Polish zloty
Czech koruna
Swedish krona
Russian rouble
Turkish lira
South Korean won
Indonesian rupiah
Hungarian forint
Danish krone
Romanian leu
Taiwan dollar
Brazilian real
Indian rupee
Croatian kuna
-20
-15
-10
-5
0
5
10
15
20
25
Source: ECB.
Note: EER-38 is the nominal effective exchange rate of the euro against the currencies of 38 of the euro area’s most important trading
partners.
The euro overnight index average (EONIA) remained stable during the review
period, at around -35 basis points. Excess liquidity increased by around €243
billion, to approximately €1,608 billion. The increase was primarily attributable to the
ECB Economic Bulletin, Issue 3 / 2017 – Update on economic and monetary developments
Financial developments
9
final targeted longer-term refinancing operation in the second series (TLTRO-II),
which resulted in a net injection of liquidity of around €200 billion (see Box 5 in this
issue of the Economic Bulletin for more details on TLTROs). In addition, the
purchases under the expanded asset purchase programme (APP) continued to
contribute to rising excess liquidity.
The EONIA forward curve has shifted downwards by around 10 basis points on
average across maturities. An initial upward movement of the curve, which lasted
until around mid-March, was more than reversed in the remainder of the review
period. Overall, the EONIA forward curve for maturities above eight years moved
downwards by around 15 basis points, while the three to seven-year segment
declined by some 10 basis points. Forward rates declined only more marginally for
shorter maturities. The curve remains below zero for maturities prior to early 2020.
ECB Economic Bulletin, Issue 3 / 2017 – Update on economic and monetary developments
Financial developments
10
3
Economic activity
The domestic demand-driven economic expansion in the euro area is firming
and broadening. Real GDP increased by 0.5%, quarter on quarter, in the fourth
quarter of 2016 (see Chart 5), on the back of positive contributions from domestic
demand and, to a lesser extent, changes in inventories. At the same time, net trade
provided a strong negative contribution to GDP growth, as import growth significantly
outpaced the rise in exports. The latest economic indicators, both hard data and
survey results, remain buoyant and point to ongoing growth in the first half of 2017,
at around the same rate as that observed in the fourth quarter of last year.
Chart 5
Euro area real GDP, the Economic Sentiment Indicator (ESI) and the composite
output Purchasing Managers’ Index (PMI)
(quarter-on-quarter percentage growth; index; diffusion index)
real GDP (right-hand scale)
ESI (left-hand scale)
composite output PMI (left-hand scale)
59
0.9
56
0.6
53
0.3
50
0.0
47
-0.3
44
-0.6
2011
2012
2013
2014
2015
2016
2017
Sources: Eurostat, European Commission, Markit and ECB.
Notes: The ESI is normalised with the mean and standard deviation of the PMI. The latest observations are for the fourth quarter of
2016 for real GDP, March 2017 for the ESI and April 2017 for the PMI.
Consumer spending rose again in the fourth quarter of 2016, thus continuing
to be an important driver of the ongoing recovery. Quarterly private consumption
growth increased further to 0.5%. This improvement in growth took place despite a
rise in the euro price of oil of almost 15% between the third and fourth quarter of last
year. On an annual basis, consumption rose by 1.9% in the fourth quarter, after 1.8%
in the third quarter. This slight increase was in contrast to a sharp slowdown in the
growth of households’ real disposable income, to 1.1%, year on year, from 1.6% in
the third quarter. This decline, in turn, mirrored the increase in annual inflation, as
measured by the private consumption deflator, between the third and fourth quarter.
It should be borne in mind, however, that income growth, despite the latest decline,
remains relatively high by historical standards. Indeed, consumer spending during
the ongoing recovery has been benefiting from rising real labour income for
households, which has primarily reflected rising employment and lower oil prices.
The slightly higher consumption growth, alongside falling real income growth,
between the third and fourth quarter resulted in a fall in the household saving rate.
ECB Economic Bulletin, Issue 3 / 2017 – Update on economic and monetary developments
Economic activity
11
Euro area labour markets continue to improve, thus supporting income and
spending. Employment rose further, by 0.3%, quarter on quarter, in the fourth
quarter of 2016, resulting in an annual increase of 1.2%. As a result, employment
currently stands 3.4% above the last trough in the second quarter of 2013. However,
compared with the pre-crisis peak in the first quarter of 2008, employment is still
down by almost half a percent. The unemployment rate in the euro area edged down
to 9.5% in February 2017, i.e. 2.6 percentage points below its post-crisis peak in
April 2013 (see Chart 6). This decline was broad-based across age and gender
groups (see also Box 2). However, the degree of underutilisation of labour remains
high and considerably above that suggested by the unemployment rate (see Box 3).
Survey information points to continued improvements in labour markets in the period
ahead.
Chart 6
Euro area employment, PMI employment expectations and unemployment
(quarter-on-quarter percentage changes; diffusion index; percentage of labour force)
employment (left-hand scale)
PMI employment expectations (left-hand scale)
unemployment rate (right-hand scale)
0.5
13.0
0.4
12.5
0.3
12.0
0.2
11.5
0.1
11.0
0.0
10.5
-0.1
10.0
-0.2
9.5
-0.3
9.0
-0.4
8.5
2011
2012
2013
2014
2015
2016
2017
Sources: Eurostat, Markit and ECB calculations.
Notes: The PMI is expressed as a deviation from 50 divided by 10. The latest observations are for the fourth quarter of 2016 for
employment, April 2017 for the PMI and February 2017 for unemployment.
Consumption growth is expected to remain robust. After having improved in the
fourth quarter of 2016, consumer confidence increased again in the first quarter. As a
result, consumer sentiment stands well above its long-term average and close to its
pre-crisis peak level in 2007. Moreover, data on retail trade (up to February 2017)
and new passenger car registrations (for the full first quarter) are in line with positive
growth in consumer spending in the first quarter of 2017, at a similar pace to that
observed in the fourth quarter. Moreover, further employment growth, as suggested
by the latest survey indicators, should also continue to support aggregate income
and consumer spending. Finally, households’ net worth relative to disposable income
continues to rise, owing largely to valuation gains on real estate holdings. This
development should add support to overall consumption growth.
Investment growth rebounded strongly in the fourth quarter, after the weak
outcome in the third quarter. Total investment rose by 3.3%, quarter on quarter, in
the fourth quarter of 2016, reflecting a strong rise in non-construction investment.
The 6.4% rise in non-construction investment was due to a sharp increase in
ECB Economic Bulletin, Issue 3 / 2017 – Update on economic and monetary developments
Economic activity
12
investment in intellectual property products, in turn reflecting the transaction of
assets by a small number of large economic operators in Ireland. By contrast,
investment in machinery and equipment contracted slightly in the fourth quarter.
Meanwhile, the small increase in construction investment, of 0.1%, quarter on
quarter, reflected a rise in investment in homes, which was partly offset by a decline
in investment in other buildings and structures.
Incoming information suggests that both business investment and
construction investment continued to rise in the first quarter of 2017.
Continued positive growth in business investment is indicated by the average level of
industrial production of capital goods in January and February, which was 0.2% up
on that in the fourth quarter of 2016. Moreover, confidence in the capital goods
sector was, on average, higher in the first quarter than in the previous quarter, and
the assessment of order books in the capital goods sector improved both overall and
in terms of orders from abroad, alongside the observed gradual improvement of the
external environment. With regard to construction investment, monthly construction
production data point to positive growth in the first quarter of 2017. Furthermore,
survey indicators on the demand situation and the assessment of order books in the
sector, as well as building permits, are still in line with positive underlying dynamics
in the short term.
The recovery in investment is expected to continue in the medium term.
Business investment is expected to be supported by domestic and external demand
and favourable financing conditions, in the context of the accommodative monetary
policy. Improving corporate profits should also support investment. As regards
construction investment, factors such as households’ rising disposable income and
improving lending conditions should underpin demand in the sector. Downside risks
to the outlook for business investment relate to remaining deleveraging needs in
some countries.
Monthly trade data point to a continued rise in euro area exports in the near
term. Total euro area exports rose by 1.8% in the fourth quarter, mainly on account
of a rebound in goods exports, supported by a weaker effective exchange rate of the
euro and a gradual rebound in global trade. Monthly trade in goods outcomes for
January and February suggest that extra-euro area exports continued to firm in the
first quarter of 2017. The export growth momentum (in three-month-on-three-month
percentage changes) seems to be driven by demand mainly from Asia (including
China) and improvements in Russia as well as the United States.
Euro area exports are expected to rebound as global trade continues to firm.
Survey indicators signal improvements in foreign demand, and new export orders
have risen. In addition, the effective exchange rate of the euro depreciated in the first
four months of 2017 and could spur competiveness gains for euro area exporters.
However, any emergence of protectionist tendencies around the world could pose
downside risks to the outlook for foreign demand and hence euro area exports in the
longer term.
Overall, the latest economic indicators are, on balance, consistent with
ongoing real GDP growth in the first and second quarters of 2017, at around
ECB Economic Bulletin, Issue 3 / 2017 – Update on economic and monetary developments
Economic activity
13
the same rate as in the fourth quarter of last year. Industrial production
(excluding construction) displayed a small decline in February 2017 following a rise
of the same magnitude in the previous month. As a result, average production over
these two months stood broadly at the same level as in the final quarter of 2016,
when production rose by 0.9% on a quarterly basis. More timely survey data are also
in line with continued positive growth dynamics in the near term. The composite
output Purchasing Managers’ Index (PMI) averaged 55.6 in the first quarter of 2017,
compared with 53.8 in the fourth quarter, before rising to 56.7 in April from 56.4 in
March (see Chart 5). At the same time, the European Commission’s Economic
Sentiment Indicator (ESI) rose to 107.9 in the first quarter from 106.9 in the fourth
quarter. Consequently, both the ESI and the PMI, which remain above their
respective long-term averages, are approaching their recent peaks at the beginning
of 2011.
Looking ahead, the ongoing economic expansion is expected to continue to
firm and broaden. The pass-through of the monetary policy measures is supporting
domestic demand and facilitates the ongoing deleveraging process. The recovery in
investment continues to benefit from very favourable financing conditions and
improvements in corporate profitability. Employment gains, which are also benefiting
from past labour market reforms, are supporting real disposable income and private
consumption. Moreover, the signs of a stronger global recovery and increasing
global trade suggest that foreign demand should increasingly add to the overall
resilience of the economic expansion in the euro area. However, economic growth
continues to be dampened by a sluggish pace of implementation of structural
reforms, in particular in product markets, and by remaining balance sheet adjustment
needs in a number of sectors. The risks surrounding the euro area growth outlook,
while moving towards a more balanced configuration, are still tilted to the downside
and relate predominantly to global factors. The results of the latest round of the
ECB’s Survey of Professional Forecasters, conducted in early April, show that
private sector GDP growth forecasts were revised upwards for 2017 and 2018 in
comparison with the previous round conducted in early January.
ECB Economic Bulletin, Issue 3 / 2017 – Update on economic and monetary developments
Economic activity
14
4
Prices and costs
Headline inflation fell back in March. After reaching 2.0% in February, headline
inflation declined to 1.5% in March (see Chart 7). The decline was driven in particular
by lower inflation rates for the volatile components energy and unprocessed food,
but also by lower HICP inflation excluding food and energy.
Chart 7
Contributions of components to euro area headline HICP inflation
(annual percentage changes; percentage point contributions)
HICP
food
energy
non-energy industrial goods
services
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0
-0.5
-1.0
-1.5
2011
2012
2013
2014
2015
2016
2017
Sources: Eurostat and ECB calculations.
Note: The latest observations are for March 2017.
Measures of underlying inflation have remained subdued. The annual rate of
HICP inflation excluding food and energy declined to 0.7% in March 2017 from 0.9%
in February – the lowest level over the last two years. The decline resulted to a large
extent from the deceleration in the very volatile travel-related components of
services. This most likely reflected mainly price effects linked to the timing of the
Easter holidays (with Easter being in April this year but in March last year), which are
thus likely to be of a more temporary nature. HICP inflation excluding food and
energy has remained well below its long-term average of 1.4%. Furthermore, most
alternative measures also do not indicate a pick-up in underlying inflationary
pressures. This may reflect in part the lagged downward indirect effects of past low
oil prices but also, more fundamentally, continued weak domestic cost pressures.
Some pipeline pressures have built up at the early stages of the production
and pricing chain. At the early stages of the pricing chain, above-average global
producer price inflation (excluding oil) and strong growth in import prices for
intermediate goods point to a build-up of pipeline price pressures. Intermediate
goods are also the main driver of recent increases in producer price inflation for total
industry (excluding construction and energy) in the euro area, which rose to 2.1% in
February 2017 from 1.5% in January. Further along the pricing chain some upward
pressure is visible in the annual inflation for import prices of non-food consumer
goods, which continued their marked pick-up since November 2016 and rose further
from -0.1% in January to 0.6% in February. However, domestic producer price
ECB Economic Bulletin, Issue 3 / 2017 – Update on economic and monetary developments
Prices and costs
15
inflation for non-food consumer goods remained largely flat at a subdued level,
recording 0.2% in February, and so has not as yet provided support for non-energy
industrial goods inflation (see also the discussion in Box 4 entitled “What can recent
developments in producer prices tell us about pipeline pressures?”).
Wage growth in the euro area has been picking up slightly, but remains low.
Annual growth in compensation per employee rose from 1.3% in the third quarter of
2016 to 1.5% in the fourth quarter, but continues to stand well below its long-term
average (since 1999) of 2.1%. Factors that may have been weighing on wage growth
include still significant slack in the labour market, weak productivity growth and the
ongoing impact of labour market reforms implemented in some countries during the
crisis. Additionally, the low inflation environment over the last years may be still
contributing to lower wage growth through backward-looking formal and informal
indexation mechanisms.
Chart 8
Market and survey-based measures of inflation expectations
(annual percentage changes)
SPF Q2 2017
SPF Q1 2017
Consensus Economics forecasts (April 2017)
ECB staff macroeconomic projections (March 2017)
market-based measures of inflation expectations (April 2017)
market-based measures of inflation expectations (January 2017)
HICP
2.0
1.5
1.0
0.5
0.0
-0.5
-1.0
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
Sources: Thomson Reuters and ECB calculations.
Note: The market-based measures of inflation expectations are derived from HICPx (the euro area HICP excluding tobacco) zero
coupon inflation-linked swaps.
Longer-term market-based inflation expectations have declined somewhat, while
survey-based measures remained stable. Since early March market-based measures
of inflation expectations have declined across all maturities (see Chart 8). The five-year
forward inflation rate five years ahead declined to around 1.6%, which is around 10 basis
points lower than the level observed in early March 2017. By contrast, the survey-based
measures for long-term inflation expectations for the euro area from the April 2017 ECB
Survey of Professional Forecasters (SPF) remained stable at 1.8%.
Residential property prices in the euro area accelerated further. According to the
ECB’s residential property price indicator, prices for houses and flats in the euro area
increased by 3.8% on a year-on-year basis in the fourth quarter of 2016, up from 3.4% in
the third quarter, which points to a strengthening and broadening of the house price
cycle.
ECB Economic Bulletin, Issue 3 / 2017 – Update on economic and monetary developments
Prices and costs
16
5
Money and credit
Broad money growth remained robust. The annual growth rate of M3 remained
broadly stable in February 2017 (at 4.7%, after 4.8% in January), hovering around a
rate of 5.0% since mid-2015 (see Chart 9). The low opportunity cost of holding liquid
deposits in an environment of very low interest rates and the impact of the ECB’s
monetary policy measures continued to support M3 growth. Annual M1 growth was
again the main contributor to M3 growth. Its pace remained stable in February (at
8.4%).
Chart 9
M3 and its counterparts
(annual percentage changes, percentage point contributions)
M3
external counterparts (net external assets)
general government debt securities held by the Eurosystem
credit to general government from MFIs excluding the Eurosystem
domestic counterparts other than credit to general government
10
8
6
4
2
0
-2
-4
-6
2013
2014
2015
2016
2017
Source: ECB.
Notes: “Domestic counterparts other than credit to general government” includes MFIs’ longer-term financial liabilities (including capital
and reserves), MFI credit to the private sector and other counterparts. The latest observation is for February 2017.
Broad money growth was again driven by domestic sources of money
creation. Purchases of debt securities in the context of the public sector purchase
programme (PSPP) continued to have a considerable positive impact on M3 growth
(see the orange bars in Chart 9). By contrast, the contribution of credit to general
government from monetary financial institutions (MFIs) excluding the Eurosystem
remained negative (see the green bars in Chart 9).
Domestic counterparts other than credit to general government also exerted a
positive impact on M3 growth (see the blue bars in Chart 9). On the one hand, this
reflects the gradual recovery in the growth of credit to the private sector. On the other
hand, the significantly negative annual rate of change in MFIs’ longer-term financial
liabilities (excluding capital and reserves) continued to support M3 growth. This is
partly explained by the flatness of the yield curve, which is linked to the ECB’s
monetary policy measures and has made it less attractive for investors to hold longterm deposits and bank bonds. The availability of the targeted longer-term
refinancing operations (TLTROs) as an alternative to longer-term market-based bank
funding also played a role.
ECB Economic Bulletin, Issue 3 / 2017 – Update on economic and monetary developments
Money and credit
17
The MFI sector’s net external asset position continued to exert downward
pressure on annual M3 growth (see the yellow bars in Chart 9). This development
reflects ongoing capital outflows from the euro area. PSPP-related sales of euro area
government bonds by non-residents make an important contribution to this trend.
The recovery in loan growth is proceeding. The annual growth rate of MFI loans
to the private sector (adjusted for sales, securitisation and notional cash pooling)
was broadly stable in February (see Chart 10). Across sectors, the annual growth of
loans to non-financial corporations (NFCs) decreased somewhat, while that of loans
to households remained stable. The significant decrease in bank lending rates seen
across the euro area since summer 2014 (owing notably to the ECB’s non-standard
monetary policy measures) and overall improvements in the supply of, and demand
for, bank loans have supported the recovery in loan growth. In addition, banks have
made progress in consolidating their balance sheets, although the level of nonperforming loans remains high in some countries and may constrain bank lending.
Chart 10
M3 and loans to the private sector
(annual rate of growth and annualised six-month growth rate)
M3 (annual growth rate)
M3 (annualised six-month growth rate)
loans to the private sector (annual growth rate)
loans to the private sector (annualised six-month growth rate)
8
6
4
2
0
-2
-4
2011
2012
2013
2014
2015
2016
2017
Source: ECB.
Notes: Loans are adjusted for loan sales, securitisation and notional cash pooling. The latest observation is for February 2017.
The April 2017 euro area bank lending survey suggests that loan growth
continued to be supported by eased lending conditions and increasing
demand across all loan categories. In the first quarter of 2017, credit standards for
loans to enterprises and for loans to households for house purchase eased slightly.
The ECB’s expanded asset purchase programme (APP) has had an easing impact
on credit terms and conditions across all loan categories. The net easing impact was
stronger for terms and conditions than for credit standards. Euro area banks reported
that the APP has contributed to an improvement of their liquidity position and their
market financing conditions. They have mainly used the liquidity obtained from the
APP to grant loans. Furthermore, the ECB’s negative deposit facility rate was said to
be having a positive effect on lending volumes, but weighing on banks’ net interest
income. Banks also reported increasing net loan demand from households and
NFCs. This increase was driven by a variety of factors, in particular the low general
ECB Economic Bulletin, Issue 3 / 2017 – Update on economic and monetary developments
Money and credit
18
level of interest rates, merger and acquisition activity and favourable housing market
prospects.
The decline in bank lending rates, which started in early 2014, has flattened at
the beginning of 2017 (see Chart 11). Between May 2014 and February 2017,
composite lending rates on loans to euro area NFCs and households fell by 117 and
106 basis points, respectively. Composite lending rates for NFCs and households
have decreased by significantly more than market reference rates since the
announcement of the ECB’s credit easing measures in June 2014. The reduction in
bank lending rates on NFC loans was especially strong in vulnerable countries,
thereby contributing to mitigating previous asymmetries in monetary policy
transmission across countries. Over the same period, the spread between interest
rates charged on very small loans (loans of up to €0.25 million) and those charged
on large loans (loans of above €1 million) in the euro area narrowed considerably.
This indicates that small and medium-sized enterprises have generally been
benefiting to a greater extent from the decline in bank lending rates than large
companies.
Chart 11
Composite bank lending rates for NFCs and households
(percentages per annum)
non-financial corporations
households for house purchase
5
4
3
2
1
2011
2012
2013
2014
2015
2016
2017
Source: ECB.
Notes: Composite bank lending rates are calculated by aggregating short and long-term rates using a 24-month moving average of
new business volumes. The latest observation is for February 2017.
The net issuance of debt securities by NFCs remained robust in the first
quarter of 2017. The latest ECB data show that the net issuance of debt securities
by euro area NFCs increased again in January and February 2017, after declining in
December 2016 mainly due to seasonal factors. Issuance activity continued to be
supported by the ECB’s purchases of non-bank investment-grade corporate bonds,
among other factors. Preliminary data suggest that issuance remained robust in
March. The net issuance of listed shares has been modest in the first months of
2017.
Financing costs for euro area NFCs are estimated to have remained favourable
in the first months of 2017. In the first quarter of 2017, the overall nominal cost of
ECB Economic Bulletin, Issue 3 / 2017 – Update on economic and monetary developments
Money and credit
19
external financing for NFCs is estimated to have barely changed compared with
December 2016. The level observed in March was only slightly above the historical
low level recorded last summer. However, the cost of equity financing remains at
high levels compared with the cost of debt financing, reflecting a relatively high
equity risk premium. By contrast, the cost of debt financing has continued to decline
in recent months, thus reaching a new historical low.
ECB Economic Bulletin, Issue 3 / 2017 – Update on economic and monetary developments
Money and credit
20
Boxes
1
The ECB’s asset purchase programme and TARGET
balances: monetary policy implementation and beyond
This box analyses the increase in TARGET balances since the start of the
asset purchase programme (APP) and explains why the current dynamics
differ from those observed during previous episodes of rising balances. 2
TARGET balances are the claims and liabilities of euro area national central banks
(NCBs) vis-à-vis the ECB that result from cross-border payments settled in central
bank money. 3 Net payment inflows into a country increase the TARGET claim (or
reduce the TARGET liability) of its NCB while net payment outflows have the
opposite effect. The total TARGET balance, which is the sum of all positive balances,
is only affected when central bank money flows between countries with positive and
negative balances. 4 Cross-border flows of central bank money, as reflected in
changes in TARGET balances, are recorded in the balance of payments of euro area
countries. 5 According to balance of payments accounting, these flows must be
mirrored in other components of the balance of payments, such as the current
account or portfolio investment flows.
Sizeable TARGET balances can be a consequence of the injection of large
amounts of excess liquidity by the euro area’s decentralised central banking
system. TARGET balances emerge when the central bank reserves created in one
jurisdiction flow to another. During the sovereign debt crisis, there was a demanddriven increase in excess liquidity as banks substituted Eurosystem funding for
market-based funding that had dried up. Although the initial provision of liquidity via
refinancing operations was TARGET-neutral, 6 TARGET balances increased as this
liquidity subsequently flowed from vulnerable to less-vulnerable countries in the
context of severe market stress. 7 Since the start of the expanded APP, however, the
2
“TARGET” stands for “Trans-European Automated Real-time Gross settlement Express Transfer
system”. In May 2008, TARGET2 fully replaced the former TARGET system as the real-time gross
settlement system owned and operated by the Eurosystem. In the interests of readability, the term
“TARGET balances” is used here to describe the balances accumulated in TARGET and TARGET2.
3
In addition, the ECB and the NCBs of five non-euro area Member States that participate in TARGET2
(Bulgaria, Croatia, Denmark, Poland and Romania) also have TARGET balances.
4
The total TARGET balance increases if, on a net basis, central bank money flows from a country with a
liability to a country with a claim, and it decreases if that money flows in the opposite direction. By
contrast, flows between two countries with claims (or two countries with liabilities) change the
composition, but not the size, of the total TARGET balance.
5
If a euro area country sends more funds abroad via TARGET than it receives, this will be offset by an
equally-sized liability of the country’s NCB vis-à-vis the ECB in the financial account of the balance of
payments under the item “other investment of the national central bank”.
6
Monetary financial institutions (MFIs) can only participate in refinancing operations through their NCB.
The liquidity is allotted to the participating MFI via a credit to its current account held at the NCB. The
implementation of refinancing operations entails no cross-border payments and is therefore TARGETneutral.
7
Under the fixed-rate, full allotment tender procedure, demand for Eurosystem refinancing was fully
accommodated subject to collateral availability, which allowed significant growth in excess liquidity. For
further discussion, see the article entitled “TARGET balances and monetary policy operations”, Monthly
Bulletin, ECB, May 2013.
ECB Economic Bulletin, Issue 3 / 2017 – Boxes
The ECB’s asset purchase programme and TARGET balances: monetary policy
implementation and beyond
21
renewed increase in excess liquidity has been predominantly supply-driven, resulting
from asset purchases by NCBs and the ECB rather than stress-related recourse to
refinancing operations. 8 The APP – and in particular the public sector purchase
programme (PSPP) – gives rise to increasing TARGET balances (see Chart A) by
inducing large cross-border liquidity flows. These flows arise (i) during APP
implementation and (ii) via further portfolio rebalancing.
Chart A
Sum of TARGET balances for the three NCBs with the largest claims and the three
with the largest liabilities
(EUR billions; end-of-month data)
countries with the largest TARGET claims (DE, LU, NL)
countries with the largest TARGET liabilities (IT, ES, PT)
1,500
1,000
500
0
-500
-1,000
2009
2010
2011
2012
2013
2014
2015
2016
2017
Source: ECB.
Notes: The three countries with the largest TARGET claims at the end of March 2017 were Germany, Luxembourg and the
Netherlands, while the three with the largest TARGET liabilities were Italy, Spain and Portugal (although the ECB’s liability is actually
greater than that of Portugal). The vertical black lines mark the commencement of purchases under the APP and the PSPP in October
2014 and March 2015, respectively. The latest data are for March 2017.
The financial structure of the euro area contributes to the current increase in
TARGET balances because cross-border payments are an inherent feature of
decentralised APP implementation in an integrated market. APP implementation
is distinct from that of refinancing operations because it can entail immediate crossborder payments, as purchases are not limited by national borders. In fact, around
80% of APP purchases by volume have involved non-domestic counterparties, while
around 50% have involved counterparties resident outside the euro area, many of
which are concentred in the United Kingdom. 9 The latter have historically accessed
TARGET2 via major euro area financial centres, particularly Germany and, to a
8
Around 85% of the increase in liquidity provided through euro-denominated open market operations
between the end of February 2015 (i.e. prior to the commencement of the PSPP) and 31 March 2017
was due to the APP. All of the increase in recourse to Eurosystem refinancing operations over the same
period reflects participation in targeted longer-term refinancing operations (TLTROs). Participation in
TLTROs should not be interpreted as a sign of stress-related recourse to Eurosystem refinancing, as
the very attractive pricing of these operations was a key motive for participation (see, for example, the
January 2017 euro area bank lending survey).
9
In this context, “non-domestic” refers to a counterparty located in a country which is different from that
of the purchasing NCB. This includes counterparties located in other euro area countries.
Counterparties may not necessarily be the legal owner of the security; they may be acting as
intermediaries, holding securities and managing transactions on behalf of the owners.
ECB Economic Bulletin, Issue 3 / 2017 – Boxes
The ECB’s asset purchase programme and TARGET balances: monetary policy
implementation and beyond
22
lesser extent, the Netherlands. 10 The main financial centres in the euro area have
always been located in countries which, during the sovereign debt crisis, came to be
viewed as less vulnerable. 11 The settlement of APP transactions is therefore
associated with structural cross-border flows to these locations. 12
The rise in the total TARGET balance has followed the upward path implied by
cross-border payments for APP transactions, suggesting that other financial
flows did not further increase the balance after the implementation of the APP.
Chart B shows how the total TARGET balance has actually evolved alongside a
simulated balance illustrating how it would have evolved if the only cross-border
payments in the system had been those stemming from APP implementation. 13 The
actual balance is currently below the simulated balance, indicating that subsequent
cross-border liquidity flows are not giving rise to additional increases in the total
TARGET balance; it instead suggests that there are some net cross-border liquidity
flows back to countries with TARGET liabilities from those with claims.
Chart B
Total TARGET balance since the launch of the PSPP and a simulated balance
(EUR billions; weekly data)
actual total TARGET balance
simulated total TARGET balance
1,400
1,300
1,200
1,100
1,000
900
800
700
600
03/15
06/15
09/15
12/15
03/16
06/16
09/16
12/16
03/17
Sources: ECB, TARGET2 and ECB staff calculations.
Notes: The simulated TARGET balance is calculated using APP transaction data and information on the location of the TARGET
accounts of APP counterparties (the ECB’s balance is treated separately from balances of non-euro area countries). The simulated
balance shows how the total TARGET balance would have evolved since March 2015 if the only cross-border payments in the system
had been the liquidity flows from central banks to counterparties’ TARGET2 accounts resulting from APP purchases. The latest data
are for March 2017.
10
The locations used by non-euro area banks for participation in TARGET2 are the result of free choice.
Banks located in the European Economic Area (EEA) that are eligible to become direct participants in
TARGET2 can choose the NCB with which they want to open a TARGET2 account, while other noneuro area banks choose correspondent banks for accessing TARGET2, typically reflecting historical
relationships. The locations have remained largely unchanged since TARGET2 went live in 2007/08.
11
Evidence from the original TARGET payment system indicates that Germany and the United Kingdom
were major financial centres well before the onset of the global financial crisis (see Cabral, I., Dierick, F.
and Vesala, J., “Banking integration in the euro area”, Occasional Paper Series, No 6, ECB, 2002). This
is still the case, but the United Kingdom is not a direct participant in TARGET2; Germany is the main
location through which UK-based banks access TARGET2, reinforcing Germany’s role as a major
financial centre.
12
For more details on how the implementation of the APP affects TARGET balances, see the box entitled
“TARGET balances and the asset purchase programme”, Economic Bulletin, Issue 7, ECB, 2016.
13
The simulation is based on transaction-level data and maps the payments from purchasing central
banks to the TARGET account used by the selling counterparties.
ECB Economic Bulletin, Issue 3 / 2017 – Boxes
The ECB’s asset purchase programme and TARGET balances: monetary policy
implementation and beyond
23
Payments related to subsequent portfolio rebalancing are also affected by the
financial structure and keep TARGET balances elevated. Since the launch of the
APP, there has been a broad-based rebalancing towards non-euro area debt
securities in the euro area as a whole which has been driven to a significant extent
by the persistently negative interest rate differentials between euro area bonds and
bonds issued by other advanced economies. 14 Euro area residents’ net purchases of
non-euro area debt securities in this period have consisted almost exclusively of debt
securities issued by other advanced economies, in particular the United States. Such
international portfolio rebalancing usually takes place through actors located in major
euro area financial centres, thereby contributing to the accumulation of reserves in
particular locations and to the persistence of TARGET balances. 15 This mechanism
is evident in the net external assets of a country’s MFIs, which mirror the
transactions of the non-banking sector with the rest of the world (see Chart C) and
the way in which the associated payment flows are channelled (see Chart D). 16 A
breakdown of MFIs’ net external assets for the largest TARGET-liability countries
shows that the payment flows associated with international portfolio rebalancing are
mainly channelled via TARGET. 17
14
See the box entitled “Analysing euro area net portfolio investment outflows”, Economic Bulletin,
Issue 2, ECB, 2017.
15
With respect to equity investment, country-level data for the largest euro area economies point to
substantial intra-euro area cross-border flows into investment funds concentrated in major euro area
financial centres, most notably Luxembourg.
16
For further details on the monetary presentation of the balance of payments, see Bê Duc, L., Mayerlen,
F. and Sola, P., “The monetary presentation of the euro area balance of payments”, Occasional Paper
Series, No 96, ECB, 2008.
17
Evidence indicates that this is the case regardless of whether the recipient of the payment is a euro
area resident. According to the ECB’s Securities Holdings Statistics (SHS), in the APP period, around
half of the increase in net purchases of foreign debt securities by residents in the three countries which
currently have the largest TARGET liability positions (i.e. Italy, Spain and Portugal) occurred vis-à-vis
non-euro area residents.
ECB Economic Bulletin, Issue 3 / 2017 – Boxes
The ECB’s asset purchase programme and TARGET balances: monetary policy
implementation and beyond
24
Chart C
Monetary presentation of the balance of payments for
the countries with the largest TARGET liabilities
Chart D
MFIs’ net external assets in the countries with the
largest TARGET liabilities – breakdown by
intermediation channel
(EUR billions; 12-month flows)
(EUR billions; 12-month flows)
MFIs' net external assets
TARGET flows
other net external assets
MFIs' net external assets
current and capital account
non-MFIs' direct investment
non-MFIs' portfolio investment
other
200
200
0
0
-200
-200
-400
-400
12/14
04/15
08/15
12/15
04/16
08/16
12/16
Source: ECB staff calculations.
Notes: Aggregate of the three countries with the largest TARGET liabilities: Italy, Spain
and Portugal. Country-level MFIs’ net external assets consist of MFIs’ positions vis-à-vis
non-euro area residents, and those vis-à-vis residents in other euro area countries (the
latter include inter-NCB positions, mainly reflecting TARGET balances). TARGET flows
reflect the 12-month difference in a country’s TARGET liabilities. Sectoral balance-ofpayments data are interpolated from quarterly data. The latest data are for December
2016.
12/14
04/15
08/15
12/15
04/16
08/16
12/16
Source: ECB staff calculations.
Notes: Aggregate of the three countries with the largest TARGET liabilities: Italy, Spain
and Portugal. Country-level MFIs’ net external assets consist of MFIs’ positions vis-à-vis
non-euro area residents, and those vis-à-vis residents in other euro area countries (the
latter include inter-NCB positions, mainly reflecting TARGET balances). TARGET flows
reflect the 12-month difference in a country’s TARGET liabilities. Sectoral balance-ofpayments data are interpolated from quarterly data. The latest data are for December
2016.
Since the launch of the PSPP, developments in the balance of payments of the
euro area countries with the largest TARGET claims and those with the largest
TARGET liabilities have differed markedly from the developments observed
during the sovereign debt crisis and have followed broadly similar patterns in
both groups. In the period from mid-2011 to mid-2012, TARGET-liability countries
experienced a sudden stop in foreign inflows to domestic MFIs and bond markets
(see Chart Ea). At the same time, domestic residents reduced their holdings of
foreign securities to repatriate liquidity, while domestic MFIs shifted funds into foreign
deposits. Moreover, TARGET-liability countries were running a combined current
account deficit. Correspondingly, TARGET-claim countries received foreign inflows to
domestic MFIs and securities, while recording a surplus in the current account. Since
the start of the PSPP, foreign investors have reduced their exposure to debt
securities in TARGET-liability countries, albeit on a markedly smaller scale than
during the sovereign debt crisis, and in a similar fashion as in TARGET-claim
countries (see Chart Eb). Moreover, residents from both country groups have
rebalanced towards foreign debt and equity securities, while recording inflows into
domestic equities. 18 Following the external adjustment process in TARGET-liability
countries over recent years, the current account has registered a surplus since the
start of the PSPP, as has continued to be the case in TARGET-claim countries.
18
Cross-border banking flows have been relatively subdued since the launch of the APP, with MFIs in
both country groups slightly reducing their foreign assets in terms of loans and deposits. In TARGET
liability countries, MFIs recorded a reduction in cross-border banking liabilities, while these increased
somewhat in TARGET claim countries.
ECB Economic Bulletin, Issue 3 / 2017 – Boxes
The ECB’s asset purchase programme and TARGET balances: monetary policy
implementation and beyond
25
Chart E
Changes in TARGET balances and selected balance of payments developments
(percentages of GDP; cumulated monthly flows)
change in TARGET balance
portfolio debt assets
portfolio debt liabilities
portfolio equity assets
portfolio equity liabilities
MFI assets (loans and deposits)
MFI liabilities (loans and deposits)
current account
a) July 2011 to August 2012
b) March 2015 to February 2017
15
15
10
10
5
5
0
0
-5
-5
-10
-10
-15
-15
-20
-20
-25
-25
TARGET liability countries
TARGET claim countries
TARGET liability countries
TARGET claim countries
Sources: ECB and ECB staff calculations.
Notes: TARGET claim countries include Germany, Luxembourg and the Netherlands. TARGET liability countries include Italy, Spain and Portugal. For assets, a positive (negative)
value indicates net purchases (sales) of foreign assets by domestic residents. For liabilities, a positive (negative) number indicates net purchases (sales) of domestic assets by
foreign residents. GDP is converted to monthly frequency.
Overall, the underlying factors driving the current increase in TARGET
balances are of an intrinsically different nature to those in previous episodes
of rising balances. The increase in TARGET balances in the period from mid-2011
to mid-2012 was triggered by a replacement of private sector funding of banks by
central bank funding in a period of stressed bank funding conditions, as also
evidenced by a range of financial market, banking and balance of payments
statistics. 19 By contrast, the current increase in TARGET balances is largely
attributable to the interplay between the decentralised implementation of the APP
and the financial structure of the euro area.
19
See the box entitled “What is driving the renewed increase in TARGET2 balances?”, Quarterly Review,
BIS, March 2017.
ECB Economic Bulletin, Issue 3 / 2017 – Boxes
The ECB’s asset purchase programme and TARGET balances: monetary policy
implementation and beyond
26
2
Recent developments in youth unemployment
The rate of youth unemployment peaked at above 24% in the euro area in 2013
for the age group 15-24. Since then, the youth unemployment rate has declined
faster than the total unemployment rate and remained around 21% in 2016, about
6 percentage points higher than in 2007. Against this background, this box describes
some key recent features of youth unemployment across the euro area countries.
A close monitoring of developments in youth unemployment is necessary in
view of potential scarring effects of unemployment, especially at the beginning
of one’s career. 20 Long periods of unemployment at a young age can result in
increased risks of future unemployment, human capital losses and lower earnings.
Youth unemployment rates are normally higher than total unemployment rates, but
large differences across countries point to potential problems with labour market
functioning in some countries.
Youth unemployment in the euro area remains above its pre-crisis level, but
the ratio of youth unemployment to total unemployment has hardly changed.
Youth unemployment is particularly high in Greece, Spain and Italy, following sharp
rises during the crisis (Chart A). However, in spite of the very high youth
unemployment rates, the ratio of youth unemployment to total unemployment did not
change significantly between 2007 and 2016, which suggests that youth
unemployment has moved in line with total unemployment (Chart B). Thus the very
high levels of youth unemployment during the crisis reflect both the intensity of the
crisis and relatively high youth unemployment in the pre-crisis period. In this regard,
it is worth noting the large heterogeneity across countries. While for the euro area as
a whole, the youth unemployment rate is 2.2 times higher than the total
unemployment rate, in Italy and Luxembourg it is more than 3 times higher. By
contrast, the youth unemployment rate in Germany is only about 1.7 times higher
than the total unemployment rate.
20
See, for instance, Arulampalam, W., “Is Unemployment Really Scarring? Effects of Unemployment
Experience on Wages”, The Economic Journal, Vol. 111, No 475, 2011, pp. 585-686.
ECB Economic Bulletin, Issue 3 / 2017 – Boxes
Recent developments in youth unemployment
27
Chart A
Youth unemployment rates in the euro area
(percentages; age group 15-24)
2007
2016
50
40
30
20
10
0
EA
GR
ES
IT
CY
PT
FR
SK
BE
FI
LU
LV
IE
SI
LT
EE
AT
MT
NL
DE
SI
AT
LT
NL
LV
DE
Source: Eurostat.
Chart B
Ratio of youth unemployment rate to total unemployment rate
(ratio; age groups: youth 15-24, total 15-74)
2007
2016
4
3
2
1
0
EA
IT
LU
BE
PT
FR
MT
SK
ES
FI
CY
IE
GR
EE
Source: Eurostat.
The youth unemployment rate is declining faster than the total unemployment
rate, reflecting its higher sensitivity to the cycle. Between 2007 and 2013 the
youth unemployment rate in the euro area increased by about 9 percentage points.
In the same period, the total unemployment rate increased by about 4.5 percentage
points (i.e. half as much as youth unemployment). Similarly, during the recovery,
youth unemployment has been declining faster than total unemployment. Between
2013 and 2016, the youth unemployment rate declined by about 3.5 percentage
points, while the total unemployment rate decreased by about 2 percentage points
(Chart C). Slovakia, Greece and Spain – followed by Portugal, Ireland and Cyprus –
are among the countries with the largest reductions in youth unemployment. In
ECB Economic Bulletin, Issue 3 / 2017 – Boxes
Recent developments in youth unemployment
28
Greece and Spain, the declines in unemployment rates were accompanied by
declines in participation rates. 21
Chart C
Change in unemployment rates since 2013
(percentage points; age groups: youth 15-24, total 15-74)
total
youth
4
2
0
-2
-4
-6
-8
-10
-12
EA
SK
ES
GR
PT
CY
IE
LT
SI
LV
EE
BE
NL
IT
MT
DE
FI
FR
AT
LU
Source: Eurostat.
The share of young unemployed remaining unemployed for more than a year
is lower than the share of total unemployed doing so. As expected, the share of
young people that remain unemployed for more than one year is smaller than the
corresponding figure for total unemployed, reflecting the fact that young people are
more prone to intermittent transitions between activity and inactivity (e.g. to acquire
further education or training). However, in some countries the share for young
unemployed is close to the share for total unemployed (Chart D).
21
There was a general decline in youth participation during the crisis. At the same time, the share of
young people who are not in education or training and also not looking for jobs has remained relatively
stable since 2007, leading to the conclusion that the decline in participation during the crisis reflects
decisions to stay in or return to education.
ECB Economic Bulletin, Issue 3 / 2017 – Boxes
Recent developments in youth unemployment
29
Chart D
Ratio of long-term youth unemployment rate to total long-term unemployment rate
(ratio; 2015)
1
0.8
0.6
0.4
0.2
0
EA
IT
SK
GR
BE
SI
MT
ES
IE
FR
LV
AT
PT
CY
DE
NL
EE
FI
Source: Eurostat.
Note: Long-term unemployment is defined as a period of unemployment of more than one year. The long-term unemployment rate is
computed as a percentage of long-term unemployed among all unemployed in the respective age group.
Overall, the relationship between youth unemployment and total
unemployment has not changed since the crisis, but the high costs associated
with youth unemployment indicate the need for policy measures to improve
labour market functioning in some countries. Such policy measures include:
(1) improving the quality and the labour market relevance of education, including via
well-developed apprenticeship systems; (2) ensuring a well-functioning and
responsible wage setting system, including when setting minimum wages;
(3) enhancing the role of public employment services and the provision of active
labour market policies with a view to supporting the unemployed during labour
market transitions and increasing their employability; (4) increasing working time
flexibility in order to facilitate a combination of work and education and to ease the
transition from education to employment in the labour market.
ECB Economic Bulletin, Issue 3 / 2017 – Boxes
Recent developments in youth unemployment
30
3
Assessing labour market slack
Despite broad improvements in euro area labour markets since the start of the
recovery and a marked decline in unemployment rates across many euro area
economies, wage growth remains subdued, suggesting that there is still a
considerable degree of labour market slack. This box looks at developments in wider
measures of labour market slack in comparison with the rather narrow definition of
the unemployment rate.
The broadening of the recovery in activity is becoming increasingly apparent
in euro area labour markets, with more countries and sectors recording
positive employment growth. Overall, the “employment-rich” recovery 22 has led to
an increase in the number of persons employed of just under five million since the
middle of 2013, offsetting virtually all of the employment losses seen over the crisis
period. Moreover, there has been a notable broadening of the labour market
recovery, as evidenced by the narrowing of the dispersion of employment growth
rates across euro area economies and sectors over the past two years, and almost
all euro area countries are now recording positive quarter-on-quarter employment
growth (see Chart A). Unemployment has declined somewhat faster than expected –
albeit still remaining high compared with pre-crisis levels (see Chart 6 in Section 3) –
and labour shortages reportedly appear to be emerging in some countries (most
notably in Germany).
Chart A
Dispersion of employment growth rates across euro area countries
(left-hand scale: weighted standard deviation; right-hand scale: percentage shares of the euro area 19)
share of euro area countries with positive quarter-on-quarter employment growth (right-hand scale)
weighted standard deviation of employment growth by country and sector (left-hand scale)
5
100%
4
80%
3
60%
2
40%
1
20%
0
0%
Q1
1999
Q1
2002
Q1
2005
Q1
2008
Q1
2011
Q1
2014
Sources: Eurostat and ECB calculations.
Note: The latest observations are for the fourth quarter of 2016.
However, despite significant increases in employment, euro area wage growth
remains subdued, suggesting that there may still be a high degree of
underutilisation of labour – or labour market “slack” – over and above the level
suggested by the unemployment rate. Chart B shows that since the start of
22
See the article entitled “The employment-GDP relationship since the crisis”, Economic Bulletin, Issue 6,
ECB, 2016.
ECB Economic Bulletin, Issue 3 / 2017 – Boxes
Assessing labour market slack
31
Economic and Monetary Union (EMU) labour shortages have typically tended to
signal rising wage pressure, but the co-movement seems to have broken down over
the course of the recovery, which may suggest that the degree of labour market
slack is still high and is containing wage growth. 23
Chart B
Co-movement of euro area labour shortages and wage growth since the start of
EMU
(left-hand scale: diffusion indices, four-quarter moving averages; right-hand scale: annual percentage changes)
labour shortages in manufacturing (left-hand scale)
labour shortages in services (left-hand scale)
compensation per employee (right-hand scale)
compensation per hour worked (right-hand scale)
6
4.0%
4
3.5%
2
3.0%
0
2.5%
-2
2.0%
-4
1.5%
-6
1.0%
-8
0.5%
Q1
1999
Q1
2002
Q1
2005
Q1
2008
Q1
2011
Q1
2014
Q1
2017
Sources: Eurostat and ECB calculations.
Notes: The labour shortages series are calculated as four quarter moving averages and have been normalised for long-term averages.
The latest observations are for the fourth quarter of 2016 (compensation per employee and compensation per hour worked) and for
the first quarter of 2017 (labour shortages indicators).
The unemployment rate is based on a rather narrow definition of labour
underutilisation. According to the International Labour Organization’s definition of
unemployment (on which the euro area headline unemployment rate is based), jobseekers are considered unemployed if they are (i) without work; (ii) available to start
working within two weeks; and (iii) actively seeking work. 24 However, wider
definitions may also be relevant for assessing the overall degree of labour market
slack, with two groups being particularly worthy of consideration: first, those who are
without work but do not meet one of the other two criteria; and, second, those who
are employed on a part-time basis but want to work more hours. The first group falls
within the inactive category and the second group within the employed category.
Currently around 3½% of the euro area working age population are marginally
attached to the labour force – that is, categorised as inactive, but simply
competing less actively in the labour market. Referred to as the “potential
additional labour force”, 25 this category comprises both (i) those who are not
currently seeking work, despite being available (mainly “discouraged” workers; and
23
This is not to deny the importance of other factors. Structural reforms to labour markets and wagebargaining systems, as well as changes in the degree of forward and backward-looking price indexing
in wage agreements and the low inflation environment, are also likely to have played a role in
interrupting this correlation.
24
See http://ec.europa.eu/eurostat/statistics-explained/index.php/Glossary:Unemployment.
25
See the Eurostat article on underemployment and potential additional labour force statistics.
ECB Economic Bulletin, Issue 3 / 2017 – Boxes
Assessing labour market slack
32
(ii) those who are actively seeking work, but are not (yet) available to begin work
(perhaps because they have received a job offer with a start date some time in the
future or because they are not able to start work within the next two weeks). The
latter sub-group currently amounts to almost 1% of the euro area working age
population, while the former sub-group is somewhat larger – currently amounting to
around 2.6% of the working age population – with the majority being discouraged
workers who are not actively seeking work because they do not think work is
available. This sub-group, however, can be relatively quick to rejoin the labour force
as labour market conditions improve. 26 While movements in the number of those
who are “available, but not seeking work” are typically countercyclical (as is
unemployment), the numbers reporting that they are “seeking work, but not
available” had been following a downward trend prior to the recovery, but have
remained flat since the rebound.
In addition, a further 3% of the working age population are currently
underemployed (i.e. working fewer hours than they would like). Part-time
employment has been rising across most euro area economies for over a decade,
mainly owing to structural factors (such as the growth in services and in part
reflecting the rise in female participation in the labour force). 27 However, a nonnegligible share of these part-time workers would like to work more hours. Currently
there are around seven million underemployed part-time workers across the euro
area – an increase of around one million since the start of the crisis. Moreover, the
number has declined only very modestly over the past two years, despite the robust
employment growth seen during the recovery.
Combining the estimates of the unemployed and the underemployed with the
broader measures of unemployment suggests that labour market slack
currently affects around 18% of the euro area extended labour force. 28 This
amount of underutilisation is almost double the level captured by the unemployment
rate, which now stands at 9.5% (see Chart C). The broader indicator is widely used
by both the US Bureau of Labor Statistics and the OECD. 29 As well as suggesting a
considerably higher estimate of labour market slack in the euro area than shown by
the unemployment rate, these broader measures have also recorded somewhat
more moderate declines over the course of the recovery than the reductions seen in
the unemployment rate.
26
However, these workers may temporarily boost unemployment levels when they re-enter the labour
force, before they find work.
27
See Box 6 entitled “Factors behind developments in average hours worked per person employed since
2008”, Economic Bulletin, Issue 6, ECB, 2016.
28
The figure is computed by expressing the numbers of unemployed and underemployed, together with
estimates of those available but not seeking work and those seeking work but not available (the
“potential additional labour force”), as a percentage of the extended labour force (i.e., the employed
and the unemployed, who comprise the active labour force, plus the potential additional labour force).
29
The US Bureau of Labor Statistics refers to this measure as the “U6” indicator. Even broader measures
are under investigation. See, for example, Hornstein, A., Kudlyak, M. and Lange, F., “Measuring
resource utilization in the labor market”, Economic Quarterly, Vol. 100(1), Federal Reserve Bank of
Richmond, 2014.
ECB Economic Bulletin, Issue 3 / 2017 – Boxes
Assessing labour market slack
33
Chart C
Broader estimates of labour underutilisation in the euro area
(percentages of the extended labour force, four-quarter moving averages)
unemployed
available, but not seeking work
seeking work, but not available
underemployed
25%
20%
15%
10%
5%
0%
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
Sources: Eurostat and ECB calculations.
Notes: All components are expressed as percentages of the extended labour force (i.e. the active labour force plus those available, but
not seeking work and those seeking work, but not available). The latest observations are for the fourth quarter of 2016.
Cross-country differences remain significant (see Chart D) – both in terms of
levels of the broader indicator and when these levels are compared with
developments in unemployment rates. In Germany, the broader indicator (and all
three of the main components) has been declining since 2013, as has the actual
unemployment rate, providing further evidence of growing tightness in the German
labour market. Elsewhere, however, these broader measures show that the degree
of labour market slack is still considerable. In France and Italy, broader measures of
labour market slack have continued to increase throughout the recovery, while in
Spain and in the other euro area economies, they have recorded some recent
declines, but remain well above pre-crisis estimates.
ECB Economic Bulletin, Issue 3 / 2017 – Boxes
Assessing labour market slack
34
Chart D
Broader estimates of labour underutilisation across euro area countries
(percentages of the respective labour force, four-quarter moving averages)
Q4 2005
Q1 2008
Q4 2013
Q4 2016
40%
35%
30%
25%
20%
15%
10%
5%
0%
Germany
France
Italy
Spain
other euro area
countries
Sources: Eurostat and ECB calculations.
Note: All measures are expressed as percentages of the extended labour force (i.e. the active labour force plus those available, but
not seeking work and those seeking work, but not available).
While these broader measures cannot be taken entirely at face value, euro area
labour markets appear subject to a greater degree of labour market slack than
the level suggested by the unemployment rate. These broader measures may
overestimate the effective degree of labour market slack somewhat, in that they
(i) overestimate the remaining capacity of underemployed part-time workers
somewhat, since a proportion of their time (typically around half) is already spent
working; (ii) may overestimate how far those in the potential additional labour force are
willing and able to find work (i.e., the extent to which they are suitably skilled for local
labour markets); and (iii) do not make allowances for the lower job finding probabilities
of many of the very long-term unemployed (i.e., those out of work for two years or
more – currently estimated to account for around one-third of the unemployment totals
across the euro area). 30 Adjustments to the broader measures to deduct the very longterm unemployed and to allow for the time that the underemployed spend working still
result in estimates of labour market slack of the order of 15% across the euro area in
the final quarter of 2016 (on a four-quarter moving average basis).
Despite a clear improvement in many labour market indicators, labour markets
in most euro area countries – with the notable exception of Germany – appear to
still be subject to a considerable degree of underutilisation. The level of the
broader indicator of labour underutilisation is still high, and this is likely to continue to
contain wage dynamics.
30
It is, for instance, well-documented that job finding probabilities differ considerably across sub-groups
of the unemployed, but it does not necessarily follow that the job finding probability of those in the
inactive category is zero – although it is, empirically, well below that of the unemployed who are
available and actively seeking work. Much depends both on the intensity of individuals’ job search and
on employers’ perceptions of the various sub-groups in the context of wider labour market conditions.
On the other hand, many of the very long-term unemployed may be similar to those in the inactive
category in terms of employability. See, for instance, Shimer, R., “The probability of finding a job,”
American Economic Review: Papers & Proceedings, Vol. 98(2), pp. 268-73, 2008; Shimer, R.,
“Reassessing the Ins and Outs of Unemployment”, Review of Economic Dynamics, Vol. 15(2),
pp. 127-48, 2012; and Hornstein, A., Kudlyak, M. and Lange, F., “Measuring resource utilization in the
labor market”, Economic Quarterly, Vol. 100(1), Federal Reserve Bank of Richmond, 2014.
ECB Economic Bulletin, Issue 3 / 2017 – Boxes
Assessing labour market slack
35
What can recent developments in producer prices tell us
about pipeline pressures?
Consumer price inflation of non-energy industrial goods in the euro area has
remained subdued thus far. The short-term outlook for this component of the
Harmonised Index of Consumer Prices (HICP) can typically be informed by what are
known as pipeline pressures. These pipeline pressures may have already emerged
at early stages in the overall pricing chain. This box discusses recent developments
in global and domestic producer prices, which are important indicators in the pricing
chain.
Pipeline pressures often have their origin at the global level. In particular,
commodity prices can pass through to euro area industrial producer prices via the
cost of imported energy (see Chart A). This pass-through can also be more indirect if
commodity prices have an impact on global non-energy producer prices. This may
subsequently also have an impact on the price of imported goods, which form part of
the supply chains used in domestic production. The annual rate of import prices for
intermediate goods is continuing to increase rapidly. This is not only a reflection of
the recovery in producer prices globally but also of the recent euro exchange rate
depreciation. The recent upturn in euro area non-energy producer price inflation
mirrors to a large extent that in global non-energy producer price inflation (see
Chart B), reflecting the use of imported intermediate goods.
Chart A
Stylised overview of the supply price chain for HICP non-energy industrial goods
exchange rate change
commodity prices
exchange rate change
import prices for
energy
euro area
4
global PPI for
industry excluding
energy
import prices for nonfood consumer
goods
import prices for
intermediate goods
PPI for intermediate
goods
exchange rate change
PPI for non-food
consumer goods
retail and distribution margins
retail and distribution margins
HICP for non-energy
industrial goods
Source: ECB illustration.
ECB Economic Bulletin, Issue 3 / 2017 – Boxes
What can recent developments in producer prices tell us about pipeline pressures?
36
Chart B
Producer prices for industry excluding energy for the euro area and globally
(annual percentage changes)
NEER-38 of the euro (inverted)
euro area PPI excluding energy and construction for domestic sales (right-hand scale)
global PPI excluding energy (trade-weighted) (right-hand scale)
15
8
6
10
4
5
2
0
0
-5
-2
-10
-4
2010
2011
2012
2013
2014
2015
2016
2017
Sources: Eurostat and ECB calculations.
Notes: The global Producer Price Index (PPI) excluding the energy sector is an ECB estimate, compiled as a weighted average for
20 euro area trading partners, using their share in the extra-euro area export of goods. To the extent possible, the series uses PPI
excluding the energy sector. For countries where this measure is not available, PPI inflation of the energy sector was subtracted from
the total PPI inflation using the energy sector’s weight in the respective economy. For a small number of countries, the contribution of
the energy sector to the overall PPI was estimated.
The latest observations are for March 2017 for the nominal effective exchange rate of the euro against the currencies of 38 of the euro
area’s main trading partners (NEER-38) and February 2017 for the PPI.
Recent producer price developments early in the pricing chain point to
evidence of some pipeline pressures. Movements in headline producer price
inflation (industry excluding construction and the energy sector) are typically
dominated by those for the intermediate goods sector, reflecting both their high
weight and amplitude (see Chart C). Headline producer prices can therefore not be
taken as a direct indicator of price pressures for final consumer price inflation.
However, the stronger and more sustained producer price developments are in
intermediate goods industries that are further upstream in the production and pricing
chain, the greater the likelihood is that they may be passed through to producer
prices in non-food consumer goods industries. Correlation analysis suggests that
producer price inflation in intermediate goods industries generally has its strongest
co-movement (at 0.7 on average) with producer price inflation in non-food consumer
goods industries with a lag of somewhat more than half a year 31; however, there
have been episodes where this co-movement lapses. The recent upturn in producer
prices for intermediate goods could hence tentatively point to some pipeline
pressures emerging at later stages over the coming months.
31
The maximum correlation may be with a lag of more than half a year. However, the impact that a
change in producer prices for intermediate goods has in a given month on producer prices for non-food
consumer goods may begin to show as early as in the initial months thereafter. A more rigorous
pass-through analysis would usefully draw on impulse responses derived from a dedicated model, but
this is beyond the scope of this box.
ECB Economic Bulletin, Issue 3 / 2017 – Boxes
What can recent developments in producer prices tell us about pipeline pressures?
37
Chart C
Producer prices for total industry and components
(annual percentage changes, percentage point contributions)
total industry, domestic sales (excluding construction and energy)
consumer food, beverages and tobacco (19%)
non-food consumer goods (12%)
capital goods (28%)
intermediates (41%)
5
4
3
2
1
0
-1
-2
2010
2011
2012
2013
2014
2015
2016
2017
Sources: Eurostat and ECB calculations.
Note: The latest observations are for February 2017.
Pipeline price pressures tend to be gradually dampened along the production
chain. It is likely that the degree of dampening depends on the number of production
stages (from crude materials to final consumption goods) and the timing of the
respective pricing decisions. One explanation is that each stage may have some
degree of manoeuvre to adjust margins and that there may be sufficient leeway in
the timing of pricing to gauge the persistence of cost shocks from upstream stages.
In this regard, firms may be making use of hedging instruments to protect
themselves against the risk of, for example, exchange rate volatility. Moreover supply
contracts can sometimes be fixed for several months ahead, thus offering a
temporary buffer against cost shocks. The relative movements in Purchasing
Managers’ Index (PMI) input and output prices in the industrial sector suggest that
there may generally be stronger variation in margins in the intermediate goods sector
than in the non-food consumer goods sector – where the latter would be at the later
stages of the pricing chain for consumer prices for non-energy industrial goods (see
Chart D). 32 At the same time the upward movement in PMI input prices has been
relatively stronger than in output prices in the non-food consumer goods sector,
which according to correlation analysis could herald a pick-up in producer price
inflation in that sector around half a year later.
32
Input costs in the PMI survey do not include labour costs and so cannot be taken as an encompassing
measure of production costs. Assessing the need and scope for adjusting margins is also difficult, since
the data provide no reliable benchmark in terms of the level of margins.
ECB Economic Bulletin, Issue 3 / 2017 – Boxes
What can recent developments in producer prices tell us about pipeline pressures?
38
Chart D
PMI survey data for intermediate goods and non-food consumer goods
(diffusion index, deviation from long-term average index value)
input prices – consumer goods excluding food, beverages and tobacco
output prices – consumer goods excluding food, beverages and tobacco
input prices – intermediate goods
output prices – intermediate goods
25
20
15
10
5
0
-5
-10
-15
-20
-25
2010
2011
2012
2013
2014
2015
2016
2017
Sources: IHS Markit and ECB calculations.
Notes: Long-term averages are calculated over the period October 2002 to March 2017. The latest observations are for March 2017.
Producer prices of non-food consumer goods industries have continued to
increase very moderately so far. Over the 12 months to February 2017, the
year-on-year growth rate of prices charged in domestic sales hovered just above
zero, while that of prices charged in sales in other euro area countries has often
even been negative (see Chart E) 33. Upward pipeline pressures for the
corresponding prices at the consumer level have recently mainly come from import
prices for non-food consumer goods, which have picked up to 0.6% year on year in
February, the first positive reading in a year. Correlation analysis suggests that
producer price inflation in non-food consumer goods industries has its strongest
co-movement (at almost 0.7) with consumer price inflation in non-energy industrial
goods with a lag of more than half a year. 34
33
Of the total non-food consumer goods produced in the euro area, about 72% are produced and sold in
the same euro area country (domestic sales) while 28% are produced in one euro area country and
sold in another euro area country (intra-euro area sales).
34
While producer and import prices for consumer goods are indicators that refer to later stages of the
pricing chain, any pressure emerging at these stages can still be enhanced or dampened by pricing
behaviour at the distribution and retailing levels. The PMI for margins in non-food retailing, one of the
few indicators available for these final stages, has hovered in a relatively narrow range in recent
months and hence does not suggest that the latest indications from producer prices for consumer
prices have been significantly blurred by any shifts in margins.
ECB Economic Bulletin, Issue 3 / 2017 – Boxes
What can recent developments in producer prices tell us about pipeline pressures?
39
Chart E
Non-energy industrial goods consumer prices and producer price and import price
inflation for non-food consumer goods for the euro area
(annual percentage change)
HICP non-energy industrial goods (left-hand scale)
import prices, non-euro area – non-food consumer goods (right-hand scale)
producer price index, domestic sales – non-food consumer goods (left-hand scale)
intra-euro area producer price index – non-food consumer goods (left-hand scale)
2.0
8
1.5
6
1.0
4
0.5
2
0.0
0
-0.5
-2
-1.0
-4
2010
2011
2012
2013
2014
2015
2016
2017
Sources: Eurostat and ECB calculations.
Note: The latest observations are for March 2017 for HICP non-energy industrial goods and February 2017 for the rest.
In summary, producer price data currently provide mixed signals regarding
pipeline pressures for HICP non-energy industrial goods prices. While it is likely
that some upward pressure has emerged at the early stages, it may take some more
time for this to filter through to the later stages of the pricing chain. It is also likely
that this upward pressure would be dampened through margin or other adjustments
along the production chain unless firms could suspend such adjustment in an
environment of strongly increasing demand. In this regard, annual growth in
production volumes remains positive despite softening somewhat in recent quarters
(see Chart F). Moreover, survey data on capacity utilisation in the non-food
consumer goods industries, to the extent that they reflect the evolution of demand
relative to supply, may point to some strengthening in pricing power.
ECB Economic Bulletin, Issue 3 / 2017 – Boxes
What can recent developments in producer prices tell us about pipeline pressures?
40
Chart F
Capacity utilisation and production in the non-food consumer goods sector
(annual percentage changes; percentages)
capacity utilisation (right-hand scale)
production index for non-food consumer goods (left-hand scale)
6
84
4
82
2
80
0
78
-2
76
-4
74
-6
72
2010
2011
2012
2013
2014
2015
2016
2017
Sources: Eurostat and ECB calculations.
Notes: The latest observations are for the fourth quarter of 2016 for production and the first quarter of 2017 for capacity utilisation. The
broken blue line refers to the long-term average for capacity utilisation, which has been calculated using data from the first quarter of
1999 to the first quarter of 2017.
ECB Economic Bulletin, Issue 3 / 2017 – Boxes
What can recent developments in producer prices tell us about pipeline pressures?
41
5
The targeted longer-term refinancing operations: an
overview of the take-up and their impact on bank
intermediation
Targeted longer-term refinancing operations (TLTROs) provide financing to
euro area credit institutions with a maturity of up to four years at attractive
conditions. Two series of operations were launched. The first series of eight
operations (TLTRO-I) was announced in June 2014. 35 It was followed by a second
series of four operations (TLTRO-II), announced in March 2016. 36 Under TLTRO-II,
banks could borrow up to 30% of the amount of their existing stock of loans to nonfinancial corporations and households (excluding loans to households for house
purchase). Moreover, banks were given the opportunity to repay funds borrowed
under TLTRO-I early and switch to TLTRO-II funds. Such a shift of funding was
rendered attractive for two reasons: first, it lengthened the maturity of bank funding
and, second, it lowered the cost as the average cost of TLTRO-I funding stands at
around 10 basis points, while the maximum rate banks will have to pay for TLTRO-II
funding is zero.
The TLTROs provide incentives for bank lending to the real economy. In the
case of TLTRO-I, the incentives for lending were two-fold. First, banks whose net
lending over a reference period exceeded a bank-specific benchmark could borrow
more in the final six TLTRO-I operations and the maximum additional amount was
set at three times the amount by which their net lending had exceeded their
benchmark. Second, banks which did not meet their lending benchmarks were
required to repay their TLTRO-I borrowings early. Incentives for lending are provided
in a different form under TLTRO-II. Rather than penalising banks that fail to meet
their benchmarks, TLTRO-II provides rewards, in the form of lower interest rates, for
banks which outperform their benchmarks. Banks whose eligible net lending in the
period between 1 February 2016 and 31 January 2018 exceeds their lending
benchmarks will benefit from a rate reduction. The TLTRO-II rate can be as low as
−40 basis points. 37
The TLTROs have eased bank funding conditions, ensuring that the monetary
policy stimulus reaches euro area households and firms. The TLTROs reduced
the marginal funding costs of banks that participated in the operations and, in
35
TLTRO-I was part of the credit easing package of measures which also included cuts in policy rates
(bringing the deposit facility rate into negative territory for the first time) and the announcement of the
intensification of work towards outright purchases of asset-backed securities. In addition, the use of the
fixed rate tender procedure with full allotment in main refinancing operations was prolonged and the
weekly fine-tuning operations to sterilise the liquidity injected under the Securities Markets Programme
were suspended.
36
For more details on TLTRO-II, see the box entitled “The second series of targeted longer-term
refinancing operations (TLTRO II)”, Economic Bulletin, Issue 3, ECB, 2016. There is a dedicated
reporting scheme for TLTRO-II to track the net lending of participating banks. Its methodology is
aligned with the methodology underpinning the MFI balance sheet statistics.
37
Counterparties will qualify for this rate (the deposit facility rate prevailing at the time of allotment for
each TLTRO-II operation) if their outstanding amounts of eligible loans (subject to certain adjustments,
for example for loan sales and purchases and for securitisation) exceeds their benchmark stocks of
eligible loans by 2.5% or more as at 31 January 2018. For amounts below this limit, the level of the
interest rate will be determined on the basis of the percentage by which a counterparty exceeds its
benchmark stock and will follow a linear graduation.
ECB Economic Bulletin, Issue 3 / 2017 – Boxes
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on bank intermediation
42
parallel, provided them with incentives to increase their supply of targeted credit. The
design of the measure thereby ensured that the reduction in funding costs that banks
benefit from is passed on to borrowers. Moreover, to the extent that market-based
funding has been replaced with TLTRO funding, the measure has contributed to a
reduction in the supply of bank bonds. All other things being equal, a decline in bank
bond issuance generally reduces banks’ bond market funding costs, further easing
funding conditions both for banks that bid in the TLTRO operations and for those that
did not. The resulting more favourable credit conditions for borrowers (when the
reduction in funding costs is passed on) in turn encourage borrowing and
expenditure for investment and consumption.
Banks’ total TLTRO-II borrowings currently stand at €739 billion. The first
TLTRO-II operation (TLTRO-II.1, settled in June 2016) attracted bids amounting to
€399 billion, which largely reflected shifts out of TLTRO-I funding and into TLTRO-II
funding (see Chart A). The second and third TLTRO-II operations (TLTRO-II.2 and
TLTRO-II.3) allotted €45 billion and €62 billion respectively. Take-up in the final
operation (TLTRO-II.4) was substantially higher at €233 billion, of which a significant
share (€216 billion) constituted a net increase in TLTRO borrowings. The significant
take-up in the final operation reflects the overall attractive pricing of TLTRO-II
compared with banks’ alternative market-based funding, and, to some degree,
incentives for back-loading take-up. 38 Overall, outstanding TLTRO credit (including
outstanding TLTRO-I credit) stood at €761 billion as at end-March 2017 and was
concentrated in the first and final TLTRO-II operations (see Charts A and B).
Chart A
Evolution of banks’ gross TLTRO borrowings
(EUR billions)
TLTRO-I
TLTRO-II
800
700
600
500
400
300
200
100
0
09/14
12/14
03/15
06/15
09/15
12/15
03/16
06/16
09/16
12/16
03/17
Source: ECB.
38
Such incentives to postpone take-up arose for several reasons. First, market participants were
expecting further cuts in policy rates at the time of bidding for TLTRO-II.1 and TLTRO-II.2. Postponing
take-up may have been preferable at that point in time in order to lock-in the lowest possible rate.
Moreover, back-loading take-up extends the tenor of the operation, over which market rates are likely
to rise. Finally, it reduces the uncertainty surrounding the final TLTRO-II rate, as banks had observed
developments in their eligible loans for half of the reference period by the time they came to bid for
TLTRO-II.4 funding. This information allowed them to make a more accurate assessment of whether
and by how much they were likely to outperform their lending benchmarks.
ECB Economic Bulletin, Issue 3 / 2017 – Boxes
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on bank intermediation
43
Chart B
Composition of outstanding TLTRO credit as at end-March 2017
TLTRO-I (1 to 8)
TLTRO-II.1
TLTRO-II.2
TLTRO-II.3
TLTRO-II.4
Source: ECB.
The joint impact of TLTRO-I and TLTRO-II on bank intermediation cannot be
easily split into the separate contributions of the two series at present. While
high bidding volumes are welcome, they do not constitute the right metric for
assessing the effectiveness of the two TLTRO series. The measure of success is
rather the improvement in funding conditions of final borrowers brought about by the
TLTROs. Given the large set of banks that bid in both series, the significant rollover
of TLTRO-I funding into TLTRO-II funding and the relatively short period since the
settlement of the first TLTRO-II operation for which bank lending data are available, it
is currently difficult to split the overall impact of the TLTROs into the contribution of
TLTRO-I and the additional impact of TLTRO-II. Instead, evidence of their joint
impact on bank intermediation is provided below.
The TLTROs, together with the other non-standard measures introduced since
June 2014, have proven effective in supporting the transmission of lower
policy rates into better borrowing conditions for the euro area non-financial
private sector. The rates on loans to non-financial corporations declined markedly
immediately after the announcement of the first series of TLTROs (see Chart C). The
declines were sharper in countries where the composite lending rates to nonfinancial corporations had been elevated, thus, overall, the cross-country dispersion
of lending rates also declined in parallel. Moreover, in vulnerable countries, banks
that borrowed under TLTRO-I reduced their rates by more than banks that abstained
from bidding. 39 Finally, banks surveyed in the euro area bank lending survey have
repeatedly reported that the TLTROs – including those of the second series – have
contributed to an easing of the terms and conditions on loans to enterprises and,
albeit to a lesser extent, an easing of credit standards (see Chart D).
39
See the article entitled “The transmission of the ECB’s recent non-standard monetary policy measures”,
Economic Bulletin, Issue 7, ECB, 2015.
ECB Economic Bulletin, Issue 3 / 2017 – Boxes
The targeted longer-term refinancing operations: an overview of the take-up and their impact
on bank intermediation
44
Chart C
Composite lending rates for non-financial corporations
(percentages per annum)
euro area
Germany
Spain
France
Italy
5
4
3
2
1
0
2010
2011
2012
2013
2014
2015
2016
2017
Source: ECB.
Notes: The vertical line denotes the announcement of the credit easing package of measures (which included TLTRO-I) in June 2014.
The latest observation is for February 2017.
Chart D
Easing impact of past TLTROs and the expected easing impact of TLTRO-II.4 on
credit standards and terms and conditions on loans to enterprises
(percentages of survey respondents indicating that the TLTROs contributed considerably and contributed somewhat to an easing of
credit standards and terms and conditions in the January 2017 euro area bank lending survey)
impact on credit standards
impact on terms and conditions
60
49
49
48
50
43
40
30
20
19
18
10
6
1
0
vulnerable countries
less vulnerable countries
past TLTROs
vulnerable countries
less vulnerable countries
TLTRO-II.4
Source: January 2017 euro area bank lending survey.
Notes: The survey responses refer to the impact of all past TLTROs and the impact of TLTRO-II.4 on bank lending conditions.
Vulnerable countries are Ireland, Greece, Spain, Italy, Cyprus, Portugal and Slovenia. Less vulnerable countries are the remaining
euro area countries.
The TLTROs seem to be supporting higher intermediation volumes in less
vulnerable euro area countries and a slowdown of the contraction in bank
lending in vulnerable countries. Chart E compares the evolution of lending to nonfinancial corporations by the set of banks that bid in both TLTRO-I and TLTRO-II with
lending by banks that did not participate in any operations in the two series of
TLTROs. Prior to the introduction of TLTRO-I net lending by both groups of banks
evolved largely in parallel. Lending by banks that did not participate in the TLTROs
ECB Economic Bulletin, Issue 3 / 2017 – Boxes
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on bank intermediation
45
appears to have remained largely unchanged subsequently. In vulnerable countries,
they have continued to shed loans at a relatively stable pace and there has been
only a very gradual slowdown in the contraction in lending by this group of banks. By
contrast, banks that bid in both series of TLTROs have significantly reduced the pace
at which they had been cutting lending to non-financial corporations. In less
vulnerable countries, bidders appear to have increased intermediation volumes.
Chart E
Lending to non-financial corporations by TLTRO bidders and non-bidders
(index: June 2014 = 1)
bidders in both TLTRO-I and TLTRO-II
non-bidders in TLTRO-I and TLTRO-II
a) vulnerable countries
b) less vulnerable countries
1.10
1.10
1.05
1.05
1.00
1.00
0.95
0.95
0.90
0.90
06/13
06/14
06/15
06/16
06/13
06/14
06/15
06/16
Source: ECB.
Notes: The chart shows the evolution of a notional stock of loans to non-financial corporations based on a sample of MFIs for which information is available at the individual MFI level.
The notional stock is constructed by adding the cumulated net flows of loans to non-financial corporations over the relevant period to the stock of loans to non-financial corporations
as at June 2013. The chart depicts the aggregate evolution of lending by the group of banks that borrowed under both TLTRO-I and TLTRO-II and lending by the group of banks
which did not access any operations in the two series. Vulnerable countries are Ireland, Greece, Spain, Italy, Cyprus, Portugal and Slovenia. Less vulnerable countries are all other
countries of the euro area. The group of bidders in vulnerable countries comprises 48 counterparties and the group of non-bidders comprises 35. In less vulnerable countries, the
group of bidders comprises 43 counterparties, while the group of non-bidders comprises 91. The data are not seasonally adjusted and therefore exhibit at times strong end-of-year
effects. The latest observation is for February 2017.
The full impact of the TLTROs is still unfolding. While the pass-through of the first
TLTRO series into improved bank lending conditions is already advanced, the full
impact of TLTRO-II is still to materialise, as the concomitant funding cost benefit for
banks is only gradually being passed on to borrowers in the form of better terms and
conditions and less stringent credit standards on new bank loans. It is also worth
recalling that the take-up in TLTRO-II.1 was by and large driven by shifts out of
TLTRO-I funding. Significant take-up net of redemptions of TLTRO-I funding
occurred only in TLTRO-II.4. The impact of this final operation is yet to materialise.
ECB Economic Bulletin, Issue 3 / 2017 – Boxes
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on bank intermediation
46
Articles
1
The slowdown in euro area productivity in a global
context
Higher labour productivity growth is a key factor in raising living standards in
advanced economies. However, labour productivity growth in the euro area has long
been low, even before the recent global slowdown. Against such a backdrop, this
article assesses the slowdown in euro area productivity growth from a wide range of
theoretical perspectives used to explain the global deceleration. These include the
role of changes in sectoral composition of the economy, the impact of the global
financial crisis, the possibility of measurement errors, a deceleration in the rate of
technological progress and diffusion, declines in business dynamism, and the
misallocation of factors of production. The article also considers more specific local
factors which may account for the longer-lasting productivity weaknesses in the euro
area, and argues that structural reforms are necessary to counter the area’s longstanding productivity deficit with the United States.
1
Introduction
Higher labour productivity growth is a key factor in raising living standards in
advanced economies. This is particularly the case in the euro area, in view of the
rapid increase projected in the age of the workforce. Recent research suggests that
while demographic effects have so far had only a modest impact on euro area
productivity growth, rates of workforce ageing over coming decades are projected to
increase, equivalent to forgoing around one-quarter of projected productivity growth
over the 2014-35 horizon. 40
Recent labour productivity growth in the euro area has, however, been low –
by both historical and international standards – albeit against the backdrop of
a generalised slowdown in global labour productivity growth. Given this broader
deceleration, considerable debate remains as to the underlying causes. Some argue
that the slowdown reflects factors which are mainly cyclical, related to the impact of
the global financial crisis, while others emphasise longer-standing structural drivers
such as changes in the sectoral composition of the economy, measurement errors, a
deceleration in the rate of technological progress and diffusion, or declines in
business dynamism and misallocation of factors of production.
40
See Aiyar, S., Ebeke, C. and Shao, X., “The impact of workforce aging on euro area productivity”, IMF
Country Report No 16/220, July 2016.
ECB Economic Bulletin, Issue 3 / 2017 – Articles
The slowdown in euro area productivity in a global context
47
This article assesses the post-crisis 41 slowdown in euro area productivity
growth from a global perspective. Section 2 presents a number of stylised facts
concerning the recent slowdown in euro area productivity growth. Section 3 provides
a growth accounting decomposition, showing that the slowdown in euro area labour
productivity growth can be traced – at least since the global financial crisis – to
reductions in the rates of both capital deepening and total factor productivity (TFP)
growth. Section 4 assesses the ability of current explanations emerging in the wider
literature to explain the global productivity slowdown, while Section 5 considers areaspecific reasons behind the ongoing productivity deficit with the United States. The
box considers the contribution of structural reforms to productivity growth and
assesses the potential role of the recently created national productivity boards.
Section 6 concludes.
2
Some stylised facts on euro area productivity growth
Regardless of the metric chosen to measure productivity, euro area labour
productivity growth has slowed markedly since the onset of the global
economic and financial crisis (see Chart 1). Over the period 2008-16, annual
growth in euro area labour productivity per person employed slowed to an average of
around 0.5% (based on a three-year moving average), from an average of around
1.1% over the course of the decade to 2007. If we consider only the post-crisis
period of recovery from 2013 to 2016, euro area labour productivity growth averaged
just 0.6% per year. Moreover, the slowdown is evident – albeit to varying degrees –
regardless of whether productivity is measured as output per person employed, as
output per hour worked, or in terms of TFP.
41
Throughout this article the “pre-crisis period” ends in 2007 and references to the “crisis period”, without
additional qualification, relate to the euro area crisis which runs from 2008 to 2012, encompassing the
two euro area recessions and the intervening period. The terms “post-crisis period” and “recovery” refer
to the period from 2013 onwards (as far as the relevant available data permit). References to the
“Great Recession” and the “global financial crisis” are to the synchronised global recession of 2008-09.
ECB Economic Bulletin, Issue 3 / 2017 – Articles
The slowdown in euro area productivity in a global context
48
Chart 1
Euro area productivity growth
(annual percentage changes, three-year moving averages; dashed lines: period averages for pre-crisis (1999 to 2007), crisis
(2008-12) and post-crisis (2013-16) intervals)
per person employed
per hour worked
TFP
2
1
0
-1
-2
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
Sources: Eurostat, the European Commission’s AMECO database and ECB staff calculations.
Note: TFP is computed from estimates of output per person employed (taken from the European Commission’s AMECO database,
which includes an estimate for 2016 on the basis of the European Commission’s Winter Forecast 2016).
Recent euro area labour productivity growth has been low, both by historical
and international standards. Chart 2 shows that the marked slowdown seen in
euro area labour productivity growth since the crisis reflects a wider generalised
trend across advanced (and emerging) economies since the Great Recession of
2008-09. Nevertheless, from the early 1990s to the present, the euro area has gone
from being one of the regions of fastest-growing labour productivity, to one of the
slowest.
Chart 2
Labour productivity growth in the euro area, the world and global regions
(annual percentage changes, three-year moving averages)
euro area
world
advanced economies
emerging market economies
United States
6
5
4
3
2
1
0
-1
1992
1995
1998
2001
2004
2007
2010
2013
2016
Sources: The Conference Board and ECB staff calculations.
Note: Labour productivity is defined as output per person employed.
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The slowdown in euro area productivity in a global context
49
The decline in euro area labour productivity growth is widespread at the sector
level, reflecting a marked slowdown in within-sector rates, rather than a shift in
industrial structure towards sectors with low labour productivity. The secular
trend towards services as an ever-greater proportion of the total economy might be
expected to result in a reduction in aggregate labour productivity growth, as
productivity growth in these sectors is typically lower than in other (mainly industrial)
sectors. However, a shift-share analysis shows that the decline in aggregate labour
productivity growth at the euro area level owes rather more to a marked slowing of
within-sector rates of labour productivity growth than to compositional effects. Using
the standard ten-sector national accounts breakdown of economic activities (NACE
A10 42), Table 1 decomposes the 0.71 percentage point decline in average annual
labour productivity growth between the pre-crisis period 1996-2007 and the period
2008-16 into (i) the share due to a slowing of within-sector rates of labour
productivity growth (holding employment shares constant at 2007 levels); (ii) the
decline due to the effects of a changing employment composition (holding sectoral
labour productivity growth at pre-crisis averages); and (iii) the cross effect, whereby
aggregate labour productivity growth is typically boosted by faster employment
growth in high labour productivity growth sectors. 43 The table shows that since the
onset of the crisis, within-sector rates of labour productivity growth have fallen
considerably, while sectoral employment shifts have slightly supported aggregate
labour productivity growth, as the labour adjustment which occurred over the crisis
was concentrated in sectors with lower productivity. The predominance of the
slowdown in within-sector rates of growth as the main driver of the aggregate
deceleration also holds if the crisis period is excluded (i.e. when considering the
2013-16 period of recovery only).
Table 1
Decomposition of the slowdown in aggregate euro area labour productivity growth:
1996-2016
(annual percentage changes; percentage point contributions to changes)
1996-2007
2008-2016
2013-2016
1.07
0.35
0.54
-0.71
-0.53
within-sector effect
-0.90
-0.80
employment composition effect
0.15
0.22
cross effect
0.08
0.02
Overall labour productivity growth
(period averages)
difference compared to 1996-2007
average
of which:
Sources: Eurostat and ECB staff calculations.
Notes: Based on a shift-share analysis using the NACE A10 sector breakdown. Published starting dates use the previous year as the
base year for growth calculations.
42
As defined in Nomenclature statistique des activités économiques dans la Communauté européenne
(Statistical classification of economic activities in the European Community (NACE)).
43
The analysis builds on work of Antipa, P., “Productivity decomposition and sectoral dynamics”,
Quarterly Selection of Articles: Banque de France Bulletin, Banque de France, Spring 2008, pp. 51-64.
ECB Economic Bulletin, Issue 3 / 2017 – Articles
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The pattern of weak labour productivity growth at the sectoral level can also
be seen using a more detailed NACE 64-sector breakdown (available on an
annual basis, currently ending in 2014). As shown in Chart 3, almost two-thirds of
the 61 sectors for which data were available show falls (often significant) in average
rates of labour productivity growth between the two periods (see the sectors to the
right of the 45° line), particularly in the manufacturing sectors and the more traded
market services (such as wholesale trade, financial and insurance services, legal
and managerial services, and travel-related services). 44
Chart 3
Pre- and post-crisis labour productivity growth by sector
(annual percentage changes (period averages); colours indicate the main NACE A10 sectoral groups; bubble sizes reflect the share of
euro area employment for each sector in 2016; sectors on the 45° line are those in which pre- and post-2013 average growth rates are
equal)
industry exclusively
construction
market services
non-market services
other sectors
6
post-crisis average
4
2
pre-crisis average
0
-2
-4
-6
-4
-2
0
2
4
6
8
10
Sources: Eurostat and ECB staff calculations.
Notes: Labour productivity is defined as output per person employed. “Pre-crisis” refers to 2000-07; “post-crisis” to 2013-14 (in line
with data availability). “Others” includes those attributed to “other services” (primarily private-sector acyclical sectors, such as arts,
entertainment and recreation activities, household services and activities of extraterritorial organisations) and agriculture, forestry and
fishing.
3
A growth accounting approach for the euro area and the
United States
3.1
Decomposing labour productivity growth
Taking a growth accounting approach shows that the post-crisis decline in
average growth in labour productivity in the euro area and the United States
can be traced back to both a marked reduction in TFP growth in comparison
with pre-crisis rates and, since 2013, a virtual absence of capital deepening.
Using data from the European Commission’s AMECO database, Chart 4
decomposes the rate of labour productivity growth for the two economies into drivers
44
Results are similar when productivity is measured on an hourly basis, although the picture is less
comprehensive, owing to the number of sectors for which data are available (only 22 out of 64).
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of capital deepening (i.e. the rate at which the capital-labour ratio is increased) and
of TFP (reflecting underlying productivity growth from greater efficiencies in
production processes and technological progress), for the pre-crisis period 19962007 and for the period 2008-16, since the onset of the global financial crisis. A
comparison between developments in both economies in the periods before and
after the onset of the Great Recession in 2008 shows, overall, (see the first two
columns for each economy in Chart 4) that the marked decline in labour productivity
growth looks to be driven by a sharp reduction in the underlying rate of TFP growth
in both economies. Over the 2008-16 period as a whole, capital deepening did not
decline in the euro area and suffered only a moderate decline in the United States;
this was largely as a result of significant shedding of labour during the depths of the
crisis period (in particular during the period 2008-09).
Chart 4
Labour productivity growth and decomposition for the euro area and the United States
(period averages of annual percentage changes and percentage point contributions*)
labour productivity growth
capital deepening contribution
TFP contribution
euro area
United States
2.0
2.0
1.5
1.5
1.0
1.0
0.5
0.5
0.0
0.0
-0.5
-0.5
1996-2007
2008-2016
2008-2012
2013-2016
1996-2007
2008-2016
2008-2012
2013-2016
Sources: The European Commission’s AMECO database and ECB staff calculations.
Notes: Productivity is measured in terms of output per person employed; * contributions are computed using a Cobb-Douglas production function, with capital deepening contributions
estimated using two-period average factor shares; TFP contribution is taken as the residual. Observations for 2016 are estimates based on the European Commission’s Winter 2016
Economic Forecast.
However, over the period 2013-16, capital deepening virtually stagnated in the
euro area and the United States. Although it is of interest to compare the periods
before and after the onset of the global financial crisis, it is worth noting that the latter
period can be divided into two distinct sub-periods. The first of these periods (200812 inclusive) was marked by strong declines in real GDP in both economies (albeit
these declines did not last as long in the United States as in the euro area, where
they spanned both the Great Recession and the sovereign debt crisis). The second
period covers the recovery, which began in 2013. Decomposing the interval since the
onset of the crisis into these two sub-periods confirms the broad slowdown in TFP
growth in the post-crisis period compared with pre-crisis averages (see the first and
final columns for each economy in Chart 4), and suggests an almost complete
absence of capital deepening in the aftermath of the crisis in both economies over
the 2013-16 interval.
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The slowdown in euro area productivity in a global context
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3.2
Factors behind the post-crisis slowdown in capital deepening
Capital deepening refers to the process of increasing the capital-labour ratio
by giving labour more capital to work with. However, the capital-labour ratio may
also indicate “artificial” capital deepening in periods of low net investment if
significant shedding of labour mechanically increases the ratio of the existing net
capital stock to a reduced workforce. Chart 5 shows that during the depths of the
crisis, both economies saw some support to capital deepening – and, indeed, a slight
increase in the rate of capital deepening in the euro area – mainly as a result of
heavy shedding of labour in some countries and sectors (which mechanically
supported capital deepening, notwithstanding markedly reduced net investment 45).
Chart 5
Capital deepening in the euro area and the United States
(annual percentage changes)
capital deepening
period averages
net investment
employment (inverted)
euro area
United States
5
5
4
4
3
3
2
2
1
1
0
0
-1
-1
-2
-2
-3
-3
1996
1998
2000
2002
2004
2006
2008
2010
2012
2014
2016
1996
1998
2000
2002
2004
2006
2008
2010
2012
2014
2016
Sources: The European Commission’s AMECO database and ECB staff calculations.
Notes: Observations for 2016 are estimates based on the European Commission’s Winter 2016 Economic Forecast. Period averages correspond to 1996-2007, 2008-12, and
2013-16, respectively.
The slowdown in capital deepening since 2013 reflects both a slower rate of
net investment and a recovery in employment growth. Net investment has
almost halved in the United States from pre-crisis rates, to around 1.7% per year
over the 2013-16 period, but has fallen much more in the euro area (and from a
lower starting rate) to just 0.6% per year – which is around one-quarter of the euro
area’s pre-crisis average annual rate of net investment. 46 However, the decline in
capital deepening in both economies since 2013 also reflects a marked offsetting
effect arising from growth in employment, which has been relatively strong in relation
to the extent of the rebound in activity. This effect has contained the rate of capital
deepening and, in fact, fully offset the low (albeit now modestly expanding) rate of
investment growth in the euro area.
45
Net of depreciation and of any accounting for obsolescence of existing capital.
46
See also the article entitled “Business investment developments in the euro area since the crisis”,
Economic Bulletin, Issue 7, ECB, 2016. The article includes a box on the implications for capital
deepening.
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A number of reasons have been put forward to explain the slowdown in capital
deepening since 2013. These include (i) the strong concentration of the recovery in
consumer-driven sectors (common to both economies) where growth is heavily
concentrated in those services that are often the most labour-intensive 47 and in
which the potential for capital-labour substitution remains somewhat limited, coupled
with a persisting weakness in investment in construction (particularly in the euro
area); (ii) the impact of the global financial crisis and ongoing credit constraints in its
aftermath (discussed in Section 4.1, below); and (iii) some further potential for offset
to the “artificial” degree of capital deepening seen over the depths of the crisis. All
three of these elements are likely to help explain the lower rates of capital deepening
seen in both the United States and euro area economies over the period of recovery.
3.3
A broader trend decline in TFP growth
Considered over the longer term, and from a more global perspective, it is the
slowdown in TFP growth which seems to have been the key contributor to the
slowdown in labour productivity growth since the mid-1990s (see Chart 6).
While estimates vary as to the magnitudes of pre- and post-crisis rates of TFP
growth seen in each of the euro area and the United States (due mainly to
differences in methodology 48), a consistent finding is that TFP growth in both has
decelerated significantly since the crisis. Including estimates for 17 other advanced
economies (see the shaded area in Chart 6), it becomes clear that a generalised
decline in average rates of TFP growth is broadly detectable across advanced
economies for the period since the mid-1990s (albeit with a modest rebound from the
negative rates of growth seen in the euro area during the depths of the global
financial crisis). Euro area TFP performance had been lacklustre, in comparison with
most advanced economies, since the mid-1990s.
47
Such as retailing, healthcare and other non-market services, and the professional and administrative
services sectors. See also the article entitled “What is behind the recent rebound in euro area
employment?” Economic Bulletin, Issue 8, ECB, 2015.
48
Estimates vary due primarily to differences in the specification of the production function underlying
growth accounting decompositions. Nevertheless, a common feature of (i) the various estimates
available for both the euro area and the United States (principally from the AMECO database and The
Conference Board, respectively), and (ii) the country-level estimates from other international
organisations such as the IMF and the OECD, is that they typically suggest a marked decline in postcrisis TFP growth rates compared with pre-crisis rates.
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The slowdown in euro area productivity in a global context
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Chart 6
Total factor productivity (TFP) growth in advanced economies
(three-year moving averages of annual percentage changes; right-hand scale: euro area and United States; left-hand scale: other
advanced economies)
euro area
euro area period averages
United States
United States period averages
range of estimates for 17 advanced economies, excluding euro area countries and United States
6
3
4
2
2
1
0
0
-2
-1
-4
1996
1998
2000
2002
2004
2006
2008
2010
2012
2014
-2
2016
Sources: The European Commission’s AMECO database and ECB staff calculations.
Notes: The shaded area shows the range of estimates for 17 advanced economies (excluding euro area countries and the United
States): Australia, Bulgaria, Canada, Croatia, Czech Republic, Denmark, Hungary, Iceland, Japan, Mexico, New Zealand, Norway,
Poland, Romania, Sweden, Switzerland and the United Kingdom. Period averages are computed for 1996-2007, 2008-12, and
2013-16, respectively.
4
Causes of the productivity slowdown: contrasting views
A range of competing explanations has been put forward in the literature to
explain the secular decline over recent years in headline productivity growth
generally, and in TFP growth in particular. This section assesses the potential of
each of these factors in helping to explain the euro area’s recent slowdown in
productivity growth in the context of the wider global deceleration.
4.1
The impact of the crisis on euro area productivity growth
The global financial crisis which began in 2008 is likely to have contributed to
the slower average rate of euro area productivity growth since the crisis.
Several mechanisms are typically associated with slower productivity growth
following financial boom-bust cycles. First, the reallocation of resources previously
associated with the build-up of housing imbalances in some euro area economies
prior to the onset of the Great Recession may be hindered by ongoing credit supply
constraints in a slow-to-recover financial system. 49 These constraints are likely to
limit the expansion of small and young, but highly productive, firms. Second,
regulatory forbearance and inadequate insolvency regimes may also lock capital into
49
See Borio, C., Kharroubi, E., Upper, C. and Zampolli, F., “Labour reallocation and productivity
dynamics: financial causes, real consequences”, BIS Working Papers, No 534, January 2016; and
Reinhart, C. and Rogoff, K., This Time is Different: Eight Centuries of Financial Folly, Princeton
University Press, 2009.
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The slowdown in euro area productivity in a global context
55
firms with low levels of productivity, so that the cleansing effects typically associated
with recessions do not occur. Lastly, risks of hysteresis, associated with protracted
periods of private sector balance sheet repair, may weaken domestic demand and
investment, thereby potentially limiting technological innovation. 50
Significant though the crisis may have been in further reducing euro area
productivity growth in the period since 2008, it does not, however, shed light
on the more fundamental issue; why euro area productivity growth was
comparatively slow (from an international perspective) before then. This can be
explained by an examination of the underlying determinants of labour productivity
growth; these are explored in detail below.
4.2
Measurement errors in outputs and inputs
It is often suggested that mismeasurement may simply underestimate the real
rate of productivity growth now seen in advanced economies. A number of
areas of concern regarding mismeasurement are discussed in the literature. These
include the mismeasurement of information and communications technology (ICT)related goods and services, arising from the difficulty in measuring improvements in
the quality of ICT hardware and software – in the United States, in particular,
measured hardware prices have recently shown falls which some see as implausibly
small in comparison with historical data. The literature also identifies the lack of an
encompassing measurement of intangible investments in the national accounts 51;
and considers the broader notion that many recent innovations are simply not
marketed and therefore not captured in the national accounts, so that the growth in
GDP, on which productivity dynamics are based, is significantly understated.
Notwithstanding the benefits of innovations associated with increasing
numbers of free digital goods and the “sharing economy”, market-based TFP
growth has slowed considerably over several consecutive decades. As Robert
Gordon notes, far-reaching welfare implications for consumers associated with
earlier innovations – including the invention of electricity and the telephone – have
been around since well before the ICT revolution. 52
Potentially more relevant are the concerns regarding inadequate measurement of
both intangible investments and improvements in the quality of ICT-related goods
and services and of labour, which may bias estimates of outputs and inputs and
result in misleading conclusions regarding labour productivity and TFP growth.
Attempts to mitigate these deficiencies are ongoing and include (i) concerted efforts
50
Recent work by staff at the IMF suggests that the crisis had a “significantly negative impact” on postcrisis euro area TFP growth. See “Gone with the headwinds: global productivity”, IMF presentation, 2
February 2017. IMF Staff discussion note to be published March/April 2017. The IMF estimates that
policy uncertainty alone is likely to have shaved around 0.1-0.2 percentage point annually from postcrisis TFP growth in advanced economies generally, with the impact in Europe being particularly
pronounced.
51
See Corrado, C., Hulten, C. and Sichel, D., “Intangible Capital and Economic Growth”, NBER Working
Paper Series, No 11948, January 2006.
52
See Gordon, R., The Rise and Fall of American Growth: The U.S. Standard of Living since the Civil
War, Princeton University Press, 2016.
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The slowdown in euro area productivity in a global context
56
aimed at creating better measures of “intangible assets” in national accounts data
sources via the inclusion of “intellectual property products” in the European System
of National and Regional Accounts (ESA 2010) 53; (ii) attempts to reassess the
development of ICT-based prices (and the wider link between ICT and productivity
growth); and (iii) greater efforts to better isolate the impact of improvements in skills.
However, given the internationally synchronised slowdown in TFP growth seen since
the onset of the Great Recession across countries at varying levels of economic
development and with differing economic structures and varying degrees of
educational attainment, mismeasurement seems unlikely to be a major cause of
either the slowdown in TFP growth which has been measured across economies, or
the marked decline in euro area TFP growth observed since the onset of the crisis.
4.3
A decline in the rate of technical progress
A widely-held view suggests that the decline in aggregate productivity growth
across advanced economies is likely due to a slowing in the rate of
technological progress across sectors, with technological innovations of
recent years simply less “revolutionary” than in the past. 54 As a result, it is
argued, recent technological innovations may simply be less pervasive compared
with earlier inventions such as the railway, electricity or the telephone, so that the
impact on TFP growth is likely to be much lower.
In a similar vein, others explain the slowdown in US TFP growth since the early
2000s as a sign that the productivity-enhancing effect of ICT innovations has
run its course – as suggested by the fact that the slowdown in US productivity
growth is most pronounced in sectors in which ICT is produced or intensively used. 55
However, many counter that the full impact of the ICT revolution has not yet been
realised and point to the potential yields from, for example, miniaturised products
with embedded connectivity, artificial intelligence, robotics, self-driving cars, drones,
3D printing, cloud services and big data, arguing that substantial gains in aggregate
productivity are likely to be seen only with a considerable lag 56. Moreover, the
argument that a slowdown in the pace of technological progress explains the marked
deceleration in euro area TFP growth since the crisis seems somewhat
unconvincing, not least because the euro area saw less of a boost to TFP growth
from the ICT revolution than that seen in, for example, the United States.
53
European System of National and Regional Accounts, which uses aggregation levels of the NACE Rev.
2 classification (2010).
54
Gordon, R., op. cit..
55
See Fernald, J., “Productivity and potential output before, during, and after the Great Recession”,
Federal Reserve Bank of San Francisco Working Paper Series, September 2012; and Cette, G.,
Fernald, J. and Mojon, B., “The pre-Great Recession slowdown in productivity” Federal Reserve Bank
of San Francisco Working Paper Series, April 2016.
56
See, for example, Mokyr, J., “Is technological progress a thing of the past?”, available at
http://voxeu.org/article/technological-progress-thing-past; Nordhaus, W., “Productivity growth and the
new economy,” NBER Working Paper Series, No 8096, January 2001; Brynjolfsson, E. and McAfee, A.,
Race Against the Machine: How the Digital Revolution is Accelerating Innovation, Driving Productivity,
and Irreversibly Transforming Employment and the Economy, Digital Frontier Press, Massachusetts,
2011; and Brynjolfsson, E. and McAfee, A., The Second Machine Age: Work, Progress, and Prosperity
in a time of Brilliant Technologies, W. W. Norton & Company, New York, 2014.
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The slowdown in euro area productivity in a global context
57
Chart 7
R&D expenditure by sector in the euro area and the United States
(Expenditure on R&D as a percentage of GDP)
business enterprise sector
government sector
higher education sector
private non-profit sector
euro area
United States
3.0
3.0
2.5
2.5
2.0
2.0
1.5
1.5
1.0
1.0
0.5
0.5
0.0
0.0
2000-2007
2008-2013
2000-2007
2008-2013
Sources: Eurostat and ECB staff calculations.
Note: Periods are limited by data availability.
More generally, the available evidence – in terms of research and development
(R&D) expenditure and high technology patents – does not point to a sharp
slowdown in global technological progress in recent years. As shown in Chart
7, R&D expenditure relative to GDP typically increased in both the euro area and the
United States following the onset of the Great Recession (i.e. during the period
2008-13 (interval limited by data availability)), suggesting that there has not been a
major decrease in the resources devoted to innovation. Similarly, while high-tech
patent applications submitted and granted have declined somewhat from their
respective peaks in the early 2000s, they remain high by historical standards (see
Chart 8). However, in explaining the euro area’s longer-term “productivity deficit” with
the United States, the higher incidence of US high-tech patenting activity per
inhabitant remains notable (as does a higher absolute number of patent applications
to the European Patent Office by US enterprises in comparison with euro area-based
firms).
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The slowdown in euro area productivity in a global context
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Chart 8
High-tech patent applications/grants in the euro area and the United States
(patent submissions/grants per million inhabitants)
euro area (right-hand scale)
United States (left-hand scale)
160
40
35
140
30
120
25
100
20
80
15
60
1996
1998
2000
2002
2004
2006
2008
2010
10
2012
Sources: Eurostat and ECB calculations.
Note: US data show patents granted by the US Patent and Trademark Office (USPTO) to US companies, while euro area data show
patent applications made to the European Patent Office (EPO) by euro area companies.
More anecdotally, there have been important technological advances in recent
years which may still bring substantial gains in aggregate productivity, albeit
with a lag. These advances are likely to enhance networking and cooperation, as
well as to increase the accessibility of products and services and the speed at which
they can be supplied.
4.4
A decline in the rate of technology diffusion and an increase in
input misallocation
Potentially more important than any possible waning of innovation may be the
fact that the pace of technology diffusion has declined, so that the latest
inventions are not incorporated into the production processes of businesses
as rapidly as in previous years. For technological innovations to have a noticeable
impact on the TFP growth of businesses, corresponding changes in organisational
structures and business models are often needed. One possible indicator of the
extent of technology diffusion, as proposed by the OECD, is the gap between the
labour productivity growth of global frontier firms – those creating the new knowledge
– and non-frontier firms, also called “laggards”, operating in the same sector. 57
According to this indicator, technology diffusion declined in the early 2000s in
advanced OECD economies (when comparable cross-country firm-level data for
Europe became available), as shown by the increasing gap in the labour productivity
performance of frontier and non-frontier firms operating in the same sector (see
Charts 9a and 9b).
The slowdown in technology diffusion has been particularly pronounced in
services in the euro area, relative to other advanced economies. Using the
57
See The future of productivity, OECD, 2015.
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The slowdown in euro area productivity in a global context
59
CompNet dataset 58 to analyse the labour productivity performance of firms in five
large euro area countries (Belgium, Spain, France, Italy and Finland) and
approximating the labour productivity growth of non-frontier firms using the
performance of the median firm 59 in each sector, Charts 9a and 9b show that the gap
in labour productivity between the frontier firms operating in the OECD and the nonfrontier firms operating in euro area countries widened prior to the global financial
crisis. Although evident in both manufacturing and services, technological diffusion
looks to have been noticeably slower in euro area services than in services in other
advanced economies. This productivity gap declined moderately during the crisis,
possibly due to the exit from the market of the least productive non-frontier firms, but
it appears to have widened again – at least as far as services are concerned.
Chart 9
Technology diffusion in manufacturing and services in selected euro area countries
(annual labour productivity growth of frontier and non-frontier firms; 2003 = 1)
frontier firms (OECD)
non-frontier firms (OECD)
non-frontier firms (euro area)
b) Services
a) Manufacturing
1.4
1.4
1.3
1.3
1.2
1.2
1.1
1.1
1.0
1.0
0.9
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
0.9
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
Source: ECB staff calculations based on OECD data and the 5th vintage of CompNet data.
Notes: The OECD frontier and non-frontier productivity developments are taken from The future of productivity, OECD, 2015. The productivity growth of the euro area is proxied as
the unweighted average, across Belgium, Spain, France, Italy and Finland, of the median firm in each 1-digit sector (using the NACE Rev. 2 classification). NACE Rev.2 1-digit
services sectors are then aggregated with value added shares.
Three key explanations for these developments are (i) the increasing
importance of so-called “tacit” learning-by-doing knowledge; (ii) a slowdown
in the rate of laggard firms’ investment in intangibles; and (iii) a decrease in
business dynamism. While the factors behind the slowdown in technology diffusion
are still not fully clear, various mutually consistent explanations have been put
forward. The literature emphasises the increasing importance of investment by firms
58
The CompNet micro-aggregated dataset is based on administrative data from company registers and
provides harmonised cross-country information on the main moments (e.g. mean, median, standard
deviation) of the distribution of a number of variables related to firm performance and competitiveness
for each sector. The data refer to firms with more than 20 employees and are population-weighted. For
details, see Lopez-Garcia, P., di Mauro, F. and the CompNet Task Force, “Assessing European
competitiveness: the new CompNet micro-based database”, Working Paper Series, No 1764, ECB,
2015.
59
The median firm was chosen since its labour productivity dynamics very closely follow the weighted
labour productivity growth average of firms that are not in the frontier in a given sector.
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The slowdown in euro area productivity in a global context
60
in human capital so as to enhance their absorptive capacity. 60 Furthermore, among
non-frontier firms, investment in intangible assets (e.g. R&D activity, firm-specific
skills and various forms of intellectual property), which is another crucial determinant
of the absorptive capacity of firms, has not kept pace with technological innovation
and is thus likely to have negatively affected technology diffusion. 61 Chart 10 shows
that the labour productivity growth gap between frontier and non-frontier firms is
larger where investments by laggards in intangibles are lower relative to investments
by frontier firms (after controlling for NACE sector).
Chart 10
Technology absorption and investment in intangibles of non-frontier firms in 11 euro
area countries
(x-axis: gap in intangibles between national frontier and laggard firms (annual average 2010-13); y-axis: gap in labour productivity
growth (annual average 2010-13))
0.1
0.08
SK
0.06
0.04
SL
PT
0.02
ES
FI
0
BE
AT
IT
FR
DE
-0.02
ET
-0.04
-0.06
-0.035
-0.03
-0.025
-0.02
-0.015
-0.01
-0.005
0
0.005
0.01
0.015
Source: ECB staff calculations based on Amadeus data.
Notes: The gap in labour productivity growth and investment in intangibles between frontier and non-frontier firms are computed at the
NACE Rev.2 2-digit sector level. Country averages are obtained with weights based on the share of each sector in total value added.
Investment in intangibles is measured as the ratio of real intangible fixed assets + depreciation over lagged real intangible fixed
assets.
A further reason for the slowdown in technological diffusion may be related to
a fall in business dynamism, or the extensiveness of “creative destruction”.
Given that young and high-growth firms can be key drivers of innovation – not least,
by exerting pressure on incumbents to innovate and become more productive, and
by speeding up labour reallocation – the literature finds a significant link between
business entry rates and technological creation and diffusion. 62 While comparable
cross-country data on business closures and openings are not available for the
entire euro area, data for the EU-14 suggest a downward trend in the rate of
business “churn” (i.e. the process of firms exiting the market and being replaced with
60
See, for example, Griffith, R., Redding, S. and Van Reenen, J., “Mapping the two faces of R&D:
productivity growth in a panel of OECD industries”, The Review of Economics and Statistics, Vol. 86,
Issue 4, November 2004, pp. 883–95.
61
See, for example, Corrado, C., Haskel, J., Jona-Lasinio, C. and Iommi, M. “Intangible capital and
growth in advanced economies: measurement methods and comparative results”, available at
http://www.intan-invest.net.
62
See, for example, Haltiwanger, J., Jarmin, R., Kulick, R. and Miranda, J., “High growth young firms:
contribution to job, output and productivity growth”, unpublished manuscript, 2016; and Baumann, U.,
and Vasardani, M., “The slowdown in US productivity – what explains it and will it persist?”, Bank of
Greece Working Paper Series, No 215, November 2016.
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The slowdown in euro area productivity in a global context
61
new firms); a similar decline has also been recorded in the United States (see Chart
11).
Chart 11
Business churn in the United States and Europe
(sum of the birth and death rates of firms)
EU-14 (left-hand scale)
United States (right-hand scale)
18.5
23
18.0
21
17.5
19
17.0
2006
2007
2008
2009
2010
2011
2012
17
2013
Source: ECB staff calculations based on US Census Bureau and Eurostat data.
Notes: EU-14 denotes the countries that had joined the EU by 1995, with the exception of Greece (given the lack of data). 2006 is the
earliest year with complete data for all EU-14 countries.
Business dynamism affects the allocation of capital and/or labour across firms
and this can have a direct impact on within-sector labour productivity growth.
A significant determinant of labour productivity growth is the degree of efficiency with
which such production inputs are allocated across firms, even within narrowly
defined sectors (“allocative efficiency”). 63 Given the heterogeneity in the performance
of firms, significant aggregate labour productivity gains can stem from the
reallocation of resources (including labour and capital) from low- to high-productivity
firms; research suggests that this may explain up to half of the aggregate labour
productivity growth in a mature economy. 64 The most frequently used, albeit
imperfect, indicator of capital and labour misallocation is the dispersion in the
marginal revenue product of capital and labour across firms within a given sector. 65
The intuition underlying this measure is that for allocative efficiency to be achieved in
a given sector where firms are assumed to face the same marginal costs, resources
should flow across firms until the marginal productivity of inputs is equalised.
However, the presence of frictions in labour, product and credit markets may hinder
reallocation and can thus significantly dampen labour productivity dynamics. The
larger the dispersion in the marginal revenue product of capital and labour, the
greater the potential drag on aggregate labour productivity growth.
63
See the article entitled “Firm heterogeneity and competitiveness in the European Union”, Economic
Bulletin, Issue 2, ECB, 2017.
64
Estimates of the relative importance of these components of TFP growth are highly dependent on the
country, sector, period and decomposition methodology used. The indicative percentages reported here
are based on selected studies on the US manufacturing sector provided in Gamberoni, E., Giordano,
C. and Lopez-Garcia, P., “Capital and labour (mis)allocation in the euro area: some stylized facts and
determinants”, Working Paper Series, No 1981, ECB, November 2016.
65
See Hsieh, C.-T. and Klenow, P.J., “Misallocation and manufacturing TFP in China and India”, The
Quarterly Journal of Economics, Vol. 124, Issue 4, November 2009, pp. 1403-48.
ECB Economic Bulletin, Issue 3 / 2017 – Articles
The slowdown in euro area productivity in a global context
62
There is evidence in several euro area countries of rising inefficiency in the
allocation of capital, compared with flatter dynamics for the misallocation of
labour. Again using the cross-country, cross-sector CompNet data, capital
misallocation appears to have been rising since the early 2000s in most euro area
countries for which data are available (with the exception of Slovakia; see Chart
12a). Moreover, this upward trend has been mainly driven by the services sectors.
Meanwhile, the increase in labour misallocation has been much less marked (see
Chart 12b), with Spain even recording a slight decrease. Similar trends have been
noted for capital and for labour in other mature economies, such as the United
States and Japan. 66
Chart 12
Developments in capital and labour misallocation in six euro area countries in the period 2002-13
(weighted averages of dispersion in the marginal revenue product across firms within a given sector; 2002 = 100)
Belgium
Spain
France
Italy
Slovakia
Finland
b) Labour misallocation
a) Capital misallocation
2.0
2.0
1.8
1.8
1.6
1.6
1.4
1.4
1.2
1.2
1.0
1.0
0.8
0.8
0.6
0.6
0.4
2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
0.4
2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
Source: ECB staff calculations based on the 5th vintage of CompNet data.
5
Additional constraints in the euro area
Aside from the global factors considered above, there may be a number of
additional European-specific factors, resulting from structural rigidities, which
help to explain the long-standing labour productivity gap between the euro
area and the United States. These may be connected with more highly regulated
product, labour and financial markets, legal and regulatory obstacles to sectoral
reallocation, or wider structural impediments such as a lower prevalence of ICTrelevant skills in the euro area. Similarly, there may be a tendency in the euro area
towards a less wholesale approach to restructuring (in order to better exploit the full
66
For Japan, see Fujii, D. and Nozawa, Y., “Misallocation of capital during Japan’s lost two decades”,
DBJ Discussion Paper Series, No 1304, June 2013. For the United States, see Hsieh, C.-T. and
Klenow, P.J., op. cit. The latter study also shows that in emerging economies such as China and India,
resource misallocation is comparatively much greater than in mature economies, but is set on a
downward trend.
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The slowdown in euro area productivity in a global context
63
range of benefits from ICT investments). Recent research has also highlighted the
role of managerial quality, given the need to reorganise production processes to
adjust to new technologies. 67
Highly regulated product and labour markets and “business unfriendly”
framework conditions constitute a significant impediment to TFP growth. In
many structural areas euro area countries are often very far from best practice. For
example, on the basis of indicators related to the “ease of doing business” –
undoubtedly, a major prerequisite for innovative and productive activity, – only one
euro area country (Finland) features in the global top ten, while many are not even
among the top 30. 68 Similarly, a simple correlation analysis shows that across the
euro area countries, a higher TFP growth trend is typically associated with better
contract enforcement mechanisms and fewer impediments to obtaining credit
(Charts 13 and 14).
Chart 13
Relationship between trend TFP growth and contract
enforcement
Chart 14
Relationship between trend TFP growth and getting
credit
(x-axis: contract enforcement (annual average 2003-15); distance to frontier, where
frontier = 100; y-axis: TFP growth (annual average 2000-15))
(x-axis: getting credit (annual average 2003-15); distance to frontier, where frontier =
100; y-axis: TFP growth (annual average 2000-15))
3.5
3.5
LV
LV
3.0
3.0
SK
SK
LT
2.5
LT
2.5
2.0
2.0
IE
IE
1.5
1.5
EE
1.0
DE
NL
BE
0.5
GR
AT
AT
PT
BE
FR
ES
CY
0.0
EE
FI
SI
1.0
DE
NL
0.5
PT
LU
GR
ES
CY
0.0
IT
FI
FR
IT
-0.5
-0.5
40
50
60
70
80
Sources: European Commission and World Bank data on enforcing contracts.
90
40
45
50
55
60
65
70
75
Sources: European Commission and World Bank data on getting credit.
Recent work by the OECD also highlights the adverse consequences of
administrative and bureaucratic impediments which are manifested in an
increase in the overall costs of debt workout (the process of repaying, restructuring
or reshaping the profile of a debt), and in impediments to firm entry and exit, which
are in turn an important determinant of cross-country differences in labour
productivity (see Box). Improvements in areas such as regulatory quality, insolvency
regimes, licencing, employment protection, public procurement rules and quality of
public administration would be likely to spur labour productivity growth in the euro
67
See, for example, Garicano, L. and Heaton. P., “Information technology, organization, and productivity
in the public sector: evidence from police departments”, Journal of Labor Economics, Vol. 28, No 1,
January 2010, pp. 167-201; and Bloom, N., Sadun, R. and Van Reenen, J. “Americans do IT better: US
multinationals and the productivity miracle” American Economic Review, Vol. 102, No 1, February
2012, pp. 167-201.
68
See Doing Business 2017: Equal Opportunity for All, World Bank, 2017.
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The slowdown in euro area productivity in a global context
64
area by improving the allocation of resources across sectors and firms, and fostering
innovation and its diffusion.
Box
The contribution of structural reforms to TFP growth and an assessment of the role of
national productivity boards
Over the past decade a growing body of literature has sought to assess the impact of structural and
institutional conditions on TFP growth. The seminal work of Aghion and Howitt 69 showed that nonfrontier countries could gain from structural policies favouring cost-efficient adoption of existing
technologies, while countries operating at the frontier would profit more from policies to promote
innovation (e.g. investment in higher education, and research and development). Robust evidence
has since been collected showing how excessive regulation in certain sectors negatively affects
TFP growth and helps explain the productivity gap between countries operating at the frontier and
the followers.
Improving institutional and structural factors can lead to higher TFP growth. Cette et al. 70 suggest
that euro area countries could achieve significantly higher TFP growth if all moved towards best
euro area practice in reducing tariff barriers and reducing employment protection, with gains
potentially largest for those countries with the most regulated markets. Work carried out by the
ECB 71 also suggests that the soundness of economic institutions (as evidenced, for example, by
application of the rule of law, control of corruption, government effectiveness, regulatory quality), the
complexity of the business environment (in terms of starting a business, obtaining credit, trading
across borders), and the level of employment protection, all contribute to the differences in TFP
performance across euro area countries.
Structural and institutional reforms can improve TFP via different channels. They improve the
allocation of resources by promoting more efficient product and labour markets and better
institutional frameworks (including those addressing insolvency). Recent OECD work 72 has shown
that improving the efficiency of insolvency regimes is a particularly important structural policy in
shaping aggregate TFP growth, as it lessens the obstacles to orderly exit for failing firms. A more
competitive and business-friendly environment also increases dynamic efficiency, as higher levels
of competition increase incentives to innovate, thus facilitating technological progress. Structural
reforms tend to reduce the labour productivity gap between those firms operating at the frontier and
the followers, since removing protection and barriers to entry promotes the diffusion of ideas to
laggard firms and encourages improvements in management quality. There could also be important
spillover effects of these reforms, particularly if they are concentrated in upstream services sectors
and lead to cost and efficiency savings for downstream producers. The role of the newly established
69
Aghion P. and Howitt P., “Joseph Schumpeter Lecture – Appropriate growth policy: A unifying
framework”, Journal of the European Economic Association Vol. 4, Nos 2-3, May 2006, pp. 269-314.
70
Cette G., Lopez, J. and Mairesse, J., “Market Regulations, Prices and Productivity”, American
Economic Review: Papers and Proceedings, Vol. 106, No 5, May 2016, pp. 104-8.
71
See, for instance, the article entitled “Increasing resilience and long-term growth: the importance of
sound institutions and economic structures for euro area countries and EMU”, Economic Bulletin, Issue
5, ECB, 2016.
72
Andrews D., Criscuolo, C. and Gal P.N., “The global productivity slowdown, technology divergence and
public policy: a firm level perspective”, Hutchins Center Working Paper Series, No 24, September
2016.
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65
national productivity boards (NPBs) may contribute to fostering productivity-enhancing reforms
across the euro area countries.
The role of national productivity boards
On 20 September 2016 the European Council recommended the establishment of productivity
boards at the national level across the EU. These boards are expected to be operational by March
2018. The recommendation was laid down in the Five Presidents’ Report 73, which stressed the
importance of convergence as a means of improving and equalising resilience of European
economic structures. NPBs are expected to be a key element of Stage 1 of European Monetary
Union deepening, which aims to strengthen the current institutional setting and encourage greater
progress of euro area countries towards best practice, leading to higher aggregate performance.
The Council’s recommendation specifies that “these boards should analyse productivity and
competitiveness developments including relative to global competitors, taking into account national
specificities and established practices.” It also stresses that the notions of productivity and
competitiveness should be considered comprehensively, paying attention to their long-term drivers
such as innovation and the capacity to attract investment, the quality of businesses and human
capital, and cost and non-cost factors. The recommendation allows for different types of institutional
design (for example the tasks of NPBs may be carried out by bodies which already exist), provided
that certain minimum requirements are met with regard to, in particular, functional independence,
analytical rigour and transparency.
Some euro area countries – for example Belgium, Germany, Ireland, France and the Netherlands –
already have bodies that perform tasks of a similar nature to the remit of the productivity boards.
While there are differences across the countries mentioned, each of these bodies generally has
both an ex ante role (in that it evaluates policies proposed by the relevant government) and an ex
post role (it monitors the implementation of such policies).
The NPBs are expected to contribute to the concrete design and foster national ownership of
productivity-enhancing structural reforms. It appears to be crucial that awareness within individual
euro area countries of the benefit of structural reforms is enhanced and that independent technical
bodies assist in the design of these reforms and in monitoring their implementation. These boards
are also expected to increase coordination of structural reform implementation at the euro area
level. To meet this objective, the Council’s recommendation proposes a regular exchange of views
and best practice among the productivity boards of the euro area countries. Moreover, it is also
envisaged that the work and recommendations of the productivity boards could be assisted by the
Commission at the supranational level, within the framework of the European Semester. Strong
information sharing, exchange of best practice and a deeper understanding of the obstacles to
higher growth in labour productivity and competitiveness should make it easier to align policies that
are both in the best interests of the European Union as a whole and which target specific needs at
the country level.
73
Juncker, J.-C. et al., Completing Europe's Economic and Monetary Union, European Commission, 22
June 2015.
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6
Concluding remarks
The slowdown in euro area productivity growth since the economic and
financial crisis is likely to stem from a combination of cyclical and secular
forces. On the cyclical side, increased uncertainty and credit restrictions arising from
the long-running crisis are likely to have held back some innovative activities and
growth of firms with high productivity, slowed the reallocation of resources from less
to more productive units, as well as reduced the willingness of firms to take on
entrepreneurial risk. Nevertheless, the marked slowdown seen since the crisis
represents the continuation of a downward trend in labour productivity growth across
advanced economies, which began in the mid-1990s.
From a longer-term perspective, labour productivity growth in the euro area
has been weak by international standards for two decades. This deficit is likely
to reflect long-standing structural rigidities – including more highly-regulated product
and labour markets – which constrain business growth and innovation in the euro
area to a greater extent than in many other advanced economies. There is now a
significant and growing body of evidence highlighting the mechanisms and extent of
“business unfriendly” administrative and bureaucratic burdens on labour productivity
growth. These relate, inter alia, to deficiencies in institutional and regulatory quality,
impediments to entry and exit of firms, limitations on credit availability, higher debt
workout costs, deficiencies in systems of contract enforcement, and the design of
employment protection legislation.
Structural reforms to boost labour productivity growth in the euro area are
particularly pressing in the light of the area’s aging population and because
the full beneficial effects of such reforms are only visible over the medium
term. While the economic recovery is firming, a reinvigoration of the reform process
is needed to translate the cyclical pick-up into a stronger trend productivity growth.
Reforms addressing key institutional weaknesses such as bottlenecks and
inefficiencies in the regulatory system, inefficiencies and waste in public
administration, poor control of corruption and malfunctioning judicial systems appear
to be critical in many euro area countries. Better debt workout mechanisms,
including enhanced efficiency of judicial processes and out-of-court mechanisms,
would help alleviate the debt burden of viable, productive firms, facilitate exit of
unviable firms and open markets for new start-ups. In many cases, these reforms are
likely to entail relatively low short term economic costs, yet are key to instilling
confidence, improving the business environment, and boosting labour productivity.
The completion of a capital markets union would also provide entrepreneurs and
innovators with alternative sources of financing for innovative projects. Finally,
enhancing further efforts to improve skills acquisition and mobility would aid sectoral
reallocation and thus allow all citizens to benefit from a higher growth economy.
By placing labour productivity growth firmly at the core of post-crisis
economic policy, the newly created National Productivity Boards could help to
increase the impetus for further structural reforms which are needed to boost
labour productivity growth in the euro area – in a sustainable way – over
coming decades. The success of the NPBs will, however, depend in large part on
the various agents involved being willing to undertake the necessary reforms.
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2
Harmonised statistics on payment services in the Single
Euro Payments Area
The annual payments statistics compiled by the European System of Central Banks
(ESCB) have recently been significantly enhanced. This is due to the need to reflect
substantial developments in the payments market in Europe, in particular the
implementation of the Single Euro Payments Area (SEPA). This article presents the
rationale for the enhancements made to the payments statistics and describes how
reporting has been improved and harmonisation increased. It provides an overview
of the results of the first production rounds in accordance with the new reporting
framework, highlighting the enhanced detail, quality, comparability and usability of
the statistics. Moreover, the article highlights the need for further updating of the
reporting requirements to keep the statistics fit for use.
Introduction
Payments statistics serve two main purposes: (i) to provide an overview to the
general public and relevant stakeholders of the world of payments in Europe in
terms of volumes, values, services, providers and systems; and (ii) to support
ESCB policy decisions in this area with relevant statistical information. Both
purposes entail updating the reporting framework to take into account the evolution
of the payments market. A new legal act 74 was adopted in late 2013, which led to the
enhancement of European payments statistics by requiring them to reflect – among
other things – changes brought about by the implementation of SEPA through the
related European legislation, in particular the Payment Services Directive 75 (PSD).
The new legislation was also aimed at better covering innovations, especially
regarding payment initiation channels. Further work on these aspects is still needed
in the light of ongoing developments in technology and legislation.
In order to enhance the legal framework for European payments statistics, a
structured merits and costs procedure was followed, which involved the relevant
ESCB committees and working groups and culminated in the final decision of
the ECB Governing Council in 2013. The procedure was set up to assess the
merits and costs of preparing new or enhanced ESCB statistics, with the aim of
minimising the reporting burden. It was launched in 2011, prior to an overhaul of the
existing statistics on payments aimed at reflecting new conditions, in particular those
created by SEPA. The procedure comprised a fact-finding exercise, a costs
assessment and a merits assessment, followed by the matching of merits and costs
related to the proposed enhancements. The fact-finding exercise enabled input to be
74
Regulation (EU) No 1409/2013 of the European Central Bank of 28 November 2013 on payments
statistics (ECB/2013/43) (OJ L 352, 24.12.2013, p. 18).
75
Directive 2007/64/EC of the European Parliament and of the Council of 13 November 2007 on payment
services in the internal market amending Directives 97/7/EC, 2002/65/EC, 2005/60/EC and 2006/48/EC
and repealing Directive 97/5/EC (OJ L 319, 5.12.2007, p. 1). See also Regulation (EU) No 260/2012 of
the European Parliament and of the Council of 14 March 2012 establishing technical and business
requirements for credit transfers and direct debits in euro and amending Regulation (EC) No 924/2009
(OJ L 94, 30.3.2012, p. 22) – often referred to as the “SEPA Regulation”.
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gathered from the perspective of data reporters, collectors and users, at both the
national and European levels; it was intended to support the development of an
appropriately defined and harmonised reporting population, and to prepare for the
costs assessment through the establishment of various options for future reporting.
The costs assessment distinguished between implementation costs and running
costs, specified per actor involved in the reporting process and per new item to be
reported. The merits assessment combined quantitative and qualitative
considerations regarding the usefulness and relevance of the data for the fulfilment
of ESCB tasks. The proposed enhancements (especially those with higher costs)
were then reviewed, taking into account their merits, in order to determine the actual
changes to implement under a cost-conscious approach. Following the merits and
costs procedure the reporting population was expanded and the reporting framework
enhanced and harmonised to ensure better comparability, in particular through
methodological changes and new breakdowns. The enhancements help ensure that
payments carried out in both SEPA and domestic formats are more closely
monitored, and that new information is set out on payment service providers and
payment services. These enhancements are analysed in more detail in the
subsequent sections of this article.
This article considers the process that led to the current enhanced payments
statistics, their implementation and prospects for further enhancements. The
second section discusses the recently enhanced legal framework. The third section
focuses on the results of the first production rounds based on the new methodology.
The fourth section concludes, looking ahead to the next review of the legal
framework for European payments statistics.
Legal framework and recent enhancements
The legal framework for the compilation of payments statistics has recently
been enhanced following the ECB’s merits and costs procedure. Up to
reference year 2013 these statistics were collected on the basis of the reporting
framework set out in an ECB Guideline 76 addressed to Eurosystem national central
banks (NCBs). In the absence of actual data, the Guideline allowed for NCBs to
compile the statistics on a best efforts basis, relying on external data sources. 77
NCBs could also provide estimates or provisional data if actual figures were not
available.
In order to increase the quality and comparability of data across countries, and
thus the usability of the statistics, it was considered necessary to harmonise
the reporting obligations and to expand the reporting population to all relevant
76
Guideline of the European Central Bank of 1 August 2007 on monetary, financial institutions and
markets statistics (recast) (ECB/2007/9) (OJ L 341, 27.12.2007, p. 1).
77
ECB Regulations concerning the collection of statistical information are legal acts directly addressed to
the relevant reporting agents. ECB Guidelines on statistics are addressed to NCBs, establishing
obligations for them to report statistics to the ECB, without having a direct legal effect on the reporting
agents and their reporting burden. Therefore, in the absence of actual data already available at the
NCB, external data sources may also be used. Consequently, the coverage of the statistics collected
based on an ECB Guideline may vary between countries owing to different reporting populations.
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institutions. Accordingly, as of reference year 2014, payments statistics have been
collected on an annual basis in line with Regulation ECB/2013/43, which is
addressed to all payment service providers and payment system operators resident
in the euro area. 78 Payment service providers (PSPs) are institutions – defined in the
PSD – which provide payment services throughout the European Union. They
comprise mainly credit institutions, electronic money institutions and payment
institutions. Payment system operators (PSOs) are legal entities that are legally
responsible for operating a payment system. In accordance with the provisions of the
Regulation, the ECB maintains and publishes a list of institutions operating in the
European Union with relevance for payments statistics. This list comprises all PSPs
and PSOs resident in EU countries.
Payments statistics are compiled through harmonised data collection
managed at the national level by each EU NCB. PSPs and PSOs report the
statistics to the NCB of the Member State of residency. The NCBs then aggregate
the data and submit the national statistics to the ECB. The latter carries out the final
level of aggregation to produce euro area and EU figures and publishes all the
datasets.
Close cooperation between the ECB and the statistical departments of the
NCBs is crucial for the production of high-quality statistics. During the annual
production rounds for payments statistics, the ECB and NCBs closely cooperate
through bilateral and multilateral interactions, and the ECB ensures the required
degree of cross-country harmonisation. In particular, in accordance with the ECB’s
Statistics Quality Framework and quality assurance procedures, the ECB carries out
a comprehensive set of quality checks on the data reported by each NCB. These
checks have been jointly developed and agreed on within the ESCB and relate to the
completeness of the data provided and the consistency of the statistics. Revisions to
previously transmitted data are also analysed and plausibility checks are performed
to detect outliers in the reported data (i.e. observations with a clearly larger or
smaller value than other observations of the time series). During the production, the
NCBs are given enough time to address the issues detected and to correct any
potentially incorrect data. More in-depth analysis of the statistics takes place outside
the regular production rounds, when there is more time for fine-tuning concepts
related to the technical reporting or the underlying methodology.
78
Additional requirements are laid down in Guideline ECB/2014/15 (as amended), addressed to euro
area NCBs. See 2014/810/EU: Guideline of the European Central Bank of 4 April 2014 on monetary
and financial statistics (recast) (ECB/2014/15) (OJ L 340, 26.11.2014, p. 1). In addition,
Recommendation ECB/2013/44 encourages the NCBs of Member States whose currency is not the
euro to implement the reporting framework set out in the Regulation. See Recommendation of the
European Central Bank of 28 November 2013 on payments statistics (ECB/2013/44) (OJ C 5,
9.1.2014, p. 1).
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Results of the first annual production rounds based on the
enhanced methodology
Compared with the previous reporting framework, several important
methodological changes – in addition to the expansion of the reporting
population – have been introduced in the enhanced statistics. Overall, the
methodology applied to both existing and new requirements has been aligned with
the definitions given in the relevant EU legislation, such as the PSD, Electronic
Money Directive 79, Regulation on MFI balance sheet statistics 80 and European
System of Accounts (ESA 2010) Regulation 81. First of all, to monitor the changes
brought about by SEPA, a new concept of residency has been adopted: the new
statistics mark a shift from using the location of the payer or the terminal 82 as the
basis for the reporting to using the residency of the PSP. This is in line with the
principle that, within SEPA, consumers, businesses and public administrations can
advantageously execute both domestic and cross-border 83 payments in euro via a
single institution and under the same conditions, irrespective of the physical location
of the payer, the payee or the PSPs involved.
The enhanced requirements now enable payments involving domestic PSPs
only to be distinguished from those also involving PSPs resident outside the
reporting country. Moreover, for all main categories of sent payments, a breakdown
by counterparty country is required when the counterparty belongs to the European
Union. Information is also requested on cross-border payments received. This
enables cross-country payment patterns within SEPA to be detected.
The statistics show that in the European Union most payments are still carried
out between PSPs resident in the same country. In particular, as can be seen in
Chart 1 below, within the euro area, around 2.5% of credit transfers and 1.7% of
direct debits initiated in 2015 were sent to an account held at a PSP resident in
another country. This means that the vast majority – above 97% – were still
domestic. For the European Union as a whole, the shares were 2.9% and 1.7%
respectively; however, data are not available for all non-euro area countries.
Compared with cross-border credit transfers and direct debits, the share of
cross-border card payments is higher for both the euro area and the European
Union: in the euro area, 7.6% of card payments sent from accounts held at euro area
PSPs were cross-border payments; for the European Union as a whole, the share
was 7.4%. Around 92.5% of EU card payments were still domestic.
79
Directive 2009/110/EC of the European Parliament and of the Council of 16 September 2009 on the
taking up, pursuit and prudential supervision of the business of electronic money institutions amending
Directives 2005/60/EC and 2006/48/EC and repealing Directive 2000/46/EC (OJ L 267, 10.10.2009,
p. 7).
80
Regulation (EU) No 1071/2013 of the European Central Bank of 24 September 2013 concerning the
balance sheet of the monetary financial institutions sector (recast) (ECB/2013/33)
(OJ L 297, 7.11.2013, p. 1).
81
Regulation (EU) No 549/2013 of the European Parliament and of the Council of 21 May 2013 on the
European system of national and regional accounts in the European Union (OJ L 174, 26.6.2013, p. 1).
82
The location of the terminal was meant to give an indication of the location of the merchant.
83
Cross-border payments are defined as payments where the PSP of the payer and that of the payee are
resident in different countries.
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Chart 1
Share of cross-border payments in the euro area and European Union in 2015
(percentages)
euro area
European Union
credit transfers
direct debits
card payments
other payments
0%
1%
2%
3%
4%
5%
6%
7%
8%
Source: ECB.
Note: The category “other payments” comprises e-money payments, cheques and other payment services, as defined in the PSD.
Overall, the new statistics illustrate how the usage of card payments has
increased in recent years to the extent that they account for almost half of all
cashless payments in the European Union. In particular, in the five-year period up
to the end of 2015, the share of card payments increased from 39% to 47% in the
European Union and from 34% to 41% in the euro area. 84 Consequently, the relative
shares of credit transfers and direct debits decreased; in 2015 the shares were 26%
and 21% respectively in the European Union and 25% and 26% respectively in the
euro area (see Chart 2) – a reduction of a few percentage points compared with five
years earlier.
84
Payments statistics for the five-year period up to the end of 2015 are available in the Statistical Data
Warehouse.
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Chart 2
Relative shares of payment services in the euro area and European Union in 2015
(percentages)
credit transfers
direct debits
card payments
other payments
euro area
25%
European Union
26%
26%
21%
41%
47%
8%
6%
Source: ECB.
Note: The category “other payments” comprises e-money payments, cheques and other payment services, as defined in the PSD.
Furthermore, the new statistics differentiate between SEPA and non-SEPA
payments for credit transfers and direct debits. A sub-category entitled “of which:
non-SEPA” has been added for reporting the total number of transactions and the
total value of transactions in relation to both credit transfers and direct debits. This is
in order to obtain information on payments made with niche products, TARGET2
payments and payments in currencies other than the euro; all of which use
non-SEPA standards. Several other new indicators have also been introduced, as
shown in the table below.
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Table
Overview of the new indicators introduced in Regulation ECB/2013/43 and Guideline ECB/2014/15*
Indicator group
New indicators
Institutions offering payment services to non-MFIs**
Information on the number of payment and e-money accounts held in credit institutions, electronic money
institutions and other PSPs
Information on the outstanding amount of e-money issued by credit institutions and other PSPs
Information on the number of payment institutions operating in the country on a cross-border basis
Payment card functions and accepting devices
Information on the number of cards on which e-money can be stored directly and on cards which give access
to e-money stored on e-money accounts
Information on the number of point-of-sale (POS) terminals, with a sub-category for e-money card terminals
Payment transactions involving non-MFIs
Geographical breakdowns for credit transfers, direct debits, card payments, e-money payments, cheques,
other payment services and total payments sent
Information on cross-border credit transfers, direct debits, e-money payments, cheques and other payment
services received
Information on non-SEPA credit transfers and direct debits
Information on credit transfers and direct debits initiated in a file/batch and on a single payment basis;
information on online banking-based credit transfers
Information on card payments initiated by electronic funds transfer at point of sale (EFTPOS) and initiated
remotely
Information on e-money payments with e-money cards and e-money accounts
Information on money remittances and transactions via telecommunication, digital or IT devices
Payment transactions per type of terminal involving non-MFIs
Geographical breakdowns for ATM cash withdrawals and deposits, POS transactions and e-money card
loading and unloading transactions
Information on e-money payments with cards with an e-money function
Payments processed by selected payment systems
Breakdowns into domestic and cross-border payments for all payment services
Activities of PSPs per type of payment service
Information on payments processed by different types of PSP per type of payment service
*2014/810/EU: Guideline of the European Central Bank of 4 April 2014 on monetary and financial statistics (recast) (ECB/2014/15) (OJ L 340, 26.11.2014, p. 1).
**Non-MFIs are natural or legal persons who do not belong to the monetary financial institutions (MFI) sector. For the purpose of payments statistics, all PSPs are excluded from the
non-MFI sector.
The harmonisation of the reporting population and underlying methodology
across countries, made possible by framing the requirements within an ECB
Regulation, has resulted in a clear improvement in the comparability and
usability of the statistics. In particular, in contrast with the previous reporting
framework, the Regulation provides a comprehensive set of data definitions to be
applied in a similar way by all reporting agents. When needed, further clarifications
have been agreed on within the applicable ESCB committees and working groups.
This is to ensure that the quality of the statistics remains high and that the national
figures can be used for country comparisons despite certain country-specific
phenomena. As an example, in Germany one-off direct debits initiated by a payment
card (electronic direct debits known as “ELV” transactions) were previously reported
under card payments. With the adoption of Regulation ECB/2013/43, as of reference
year 2014 these transactions have been reported as direct debits, in accordance
with the underlying payment service used. This change enhances the comparability
of data across countries.
The new requirements have significantly enhanced the quality and overall
comparability of the statistics, and remaining instances of inconsistency
within and across countries are expected to be phased out soon. These
inconsistencies are mainly due to the reclassification of certain data. Relevant
examples are offered by the figures on credit transfers, direct debits and book entries
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in selected countries. Chart 3 below shows the impact of the reclassification of book
entries from direct debits to the category “debits from the accounts by simple book
entry” as of reference year 2014, which is mostly explained by the changes in
reporting implemented by German and Austrian PSPs. Simple book entries are
payments initiated by a PSP without a specific transaction order so as to credit or
debit a customer’s account without the use of a payment instrument. According to
the PSD, these are not payment services and are therefore not included in credit
transfers or direct debits.
Chart 3
Number of direct debits and debits from the accounts by simple book entry in the
European Union
(billions)
direct debits
debits from the accounts by simple book entry
25
20
15
10
5
0
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
Source: ECB.
Finally, the expectation is for the homogeneity and reliability of data to
increase over time. In fact, different practices followed by NCBs have been further
harmonised through continuous dialogue within the central bank community and with
the reporting agents.
Next review of the ECB legal framework for European payments
statistics
As technological, regulatory and other developments are impacting the retail
payments landscape, the data collection may be reviewed at regular intervals
and adjusted to market developments to keep the statistics fit for use. The
need to continuously enhance statistical reporting in the field of payments is felt
worldwide. The ECB and some Eurosystem NCBs are actively contributing to the
work underway at the global level in their capacity as members of the Committee on
Payments and Market Infrastructures (CPMI) of the Bank for International
Settlements (BIS). Methodological and definitional issues are currently being
analysed by CPMI members, and a more general restructuring of the format of the
statistics on payments, clearing and settlement systems published by the BIS is
underway. This restructuring and analysis includes the removal of obsolete
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information and the addition of new indicators relevant in analysing today’s evolving
landscape. Payments fall within the scope of this analysis, especially considering the
high relevance of technological advances and regulatory changes for the demand
and the supply side of the payment industry.
Box
Business developments in the field of payment services in Europe
Notable examples of potential information to be captured following further enhancements to
payments statistics, from a technological perspective, include (i) whether a card payment was made
in a contact or contactless mode, (ii) whether a mobile device was used for making a
person-to-person (P2P) or consumer-to-business (C2B) payment, and (iii) whether the payment
was made using an e-commerce payment solution. Other relevant developments that enhanced
payments statistics may help monitor are those related to instant payments, i.e. immediate or
close-to-immediate transfers of reusable funds between end users, with 24/7/365 availability. From
a regulatory perspective, the revised Payment Services Directive (EU/2015/2366) 85 (or “PSD2”) has
introduced two new payment services, payment initiation services and account information services,
which may generate the need to collect additional data from payment service providers.
New or existing means of performing payments must first and foremost be secure. Enhanced
payments statistics could also support the ECB and central banks in monitoring fraud levels as part
of their oversight of payment instruments. The PSD2 has reinforced the need for fraud monitoring
and requires PSPs to collect and report data on fraud relating to different means of payment. 86
In any case, further enhancements to the payments statistics intended to cover the above
developments would need to take into account the timeline for the PSD2 and the related Regulatory
Technical Standards to become applicable and for harmonised instant payments in euro to become
available to end users.
As was the case with the latest update of the reporting framework, where a
new or substantially enhanced regulation on statistics is introduced by the
ECB, it will always be preceded by a systematic assessment of the merits and
costs associated with collecting the new data. 87 A review of the appropriateness
of current reporting requirements, i.e. a post-implementation assessment, is also part
of the procedure. The purpose is to evaluate the continued relevance of the
statistics, in particular whether they adequately meet the specified needs of the
users and the new requirements. In the initial stage of the procedure the existing
85
Directive (EU) 2015/2366 of the European Parliament and of the Council of 25 November 2015 on
payment services in the internal market, amending Directives 2002/65/EC, 2009/110/EC and
2013/36/EU and Regulation (EU) No 1093/2010, and repealing Directive 2007/64/EC (OJ L 337,
23.12.2015, p. 35).
86
In this respect, overlaps and dual reporting to different authorities should be avoided.
87
Any future review of the reporting framework will be preceded by a merits and costs procedure before a
decision is made as to the actual implementation of the proposed changes.
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methodology can also be updated. 88 The implementation of any additional new
requirements would increase the burden on businesses and should therefore be
preceded by a careful, in-depth assessment of the associated benefits and costs.
Conclusions
Significant developments in the European payments market called for the
payments statistics compiled by the ESCB to be updated. Following a merits
and costs procedure carried out in 2011 and 2012, a new ECB Regulation on
payments statistics was introduced at end-2013. Correspondingly, as of reference
year 2014, all payment service providers and payment system operators resident in
the euro area have been obliged to report the information included in the Regulation
to the NCB of the Member State of residency.
In addition to the expansion of the reporting population, several new
requirements and enhancements were added to keep the statistics fit for
purpose. Existing definitions and concepts were clarified and aligned with the
relevant European legislation. The ECB and NCBs cooperated closely in making
these amendments.
The new statistics show that the quality and comparability of the data has
improved thanks to more harmonised reporting across countries and
institutions. However, some inconsistencies still exist across countries. These are
currently being analysed and further clarification is being provided. Overall, it is
expected that the homogeneity and reliability of the data will increase over time.
In order to keep pace with the technological, regulatory and other
developments that are impacting the payments landscape in Europe, the
statistical reporting requirements may be reviewed at regular intervals.
In addition to European payments statistics, the ECB and some Eurosystem
NCBs are also contributing to the work related to updating statistics on
payments, clearing and settlement systems in CPMI countries published by
the BIS.
88
For instance, the definition of cross-border card payments used in payments statistics may need to be
fine-tuned to take into account the definition introduced in the Regulation on interchange fees for
card-based payment transactions. See Regulation (EU) 2015/751 of the European Parliament and of
the Council of 29 April 2015 on interchange fees for card-based payment transactions (OJ L 123,
19.5.2015, p. 1).
ECB Economic Bulletin, Issue 3 / 2017 – Articles
Harmonised statistics on payment services in the Single Euro Payments Area
77
Statistics
Contents
1 External environment
S2
2 Financial developments
S3
3 Economic activity
S8
4 Prices and costs
S 14
5 Money and credit
S 18
6 Fiscal developments
S 23
Further information
ECB statistics can be accessed from the Statistical Data Warehouse (SDW):
http://sdw.ecb.europa.eu/
Data from the statistics section of the Economic Bulletin are available from the SDW:
http://sdw.ecb.europa.eu/reports.do?node=1000004813
A comprehensive Statistics Bulletin can be found in the SDW:
http://sdw.ecb.europa.eu/reports.do?node=1000004045
Methodological definitions can be found in the General Notes to the Statistics Bulletin:
http://sdw.ecb.europa.eu/reports.do?node=10000023
Details on calculations can be found in the Technical Notes to the Statistics Bulletin:
http://sdw.ecb.europa.eu/reports.do?node=10000022
Explanations of terms and abbreviations can be found in the ECB’s statistics glossary:
http://www.ecb.europa.eu/home/glossary/html/glossa.en.html
Conventions used in the tables
-
data do not exist/data are not applicable
.
data are not yet available
...
nil or negligible
(p)
provisional
s.a.
seasonally adjusted
n.s.a.
non-seasonally adjusted
ECB Economic Bulletin, Issue 3 / 2017 - Statistics
S1
1 External environment
1.1 Main trading partners, GDP and CPI
GDP 1)
(period-on-period percentage changes)
G20 2)
United
United
States Kingdom
Japan
China
CPI
(annual percentage changes)
Memo item:
euro area
OECD countries
Total
excluding food
and energy
United
United
States Kingdom
(HICP)
Japan
China
Memo item:
euro area 3)
(HICP)
1
2
3
4
5
6
7
8
9
10
11
12
13
2014
2015
2016
3.4
3.4
3.1
2.4
2.6
1.6
3.1
2.2
1.8
0.2
1.2
1.0
7.3
6.9
6.7
1.2
2.0
1.8
1.7
0.6
1.1
1.8
1.7
1.8
1.6
0.1
1.3
1.5
0.0
0.7
2.7
0.8
-0.1
2.0
1.4
2.0
0.4
0.0
0.2
2016 Q1
Q2
Q3
Q4
0.8
0.8
0.8
0.9
0.2
0.4
0.9
0.5
0.2
0.6
0.5
0.7
0.5
0.5
0.3
0.3
1.3
1.9
1.8
1.7
0.6
0.3
0.4
0.5
1.0
0.8
1.0
1.5
1.9
1.8
1.8
1.8
1.1
1.0
1.1
1.8
0.4
0.4
0.7
1.2
0.0
-0.4
-0.5
0.3
2.1
2.1
1.7
2.2
0.0
-0.1
0.3
0.7
2016 Oct.
Nov.
Dec.
-
-
-
-
-
-
1.4
1.5
1.8
1.7
1.7
1.8
1.6
1.7
2.1
0.9
1.2
1.6
0.1
0.5
0.3
2.1
2.3
2.1
0.5
0.6
1.1
2017 Jan.
Feb.
Mar.
-
-
-
-
-
-
2.3
2.5
.
1.9
1.9
.
2.5
2.7
2.4
1.8
2.3
2.3
0.4
0.3
.
2.5
0.8
0.9
1.8
2.0
1.5
Sources: Eurostat (col. 3, 6, 10, 13); BIS (col. 2, 4, 9, 11, 12); OECD (col. 1, 5, 7, 8).
1) Quarterly data seasonally adjusted; annual data unadjusted.
2) Data for Argentina are currently not available owing to the state of emergency in the national statistical system declared by the government of Argentina on 7 January 2016. As a
consequence, Argentina is not included in the calculation of the G20 aggregate. The policy regarding the inclusion of Argentina will be reconsidered in the future depending on
further developments.
3) Data refer to the changing composition of the euro area.
1.2 Main trading partners, Purchasing Managers’ Index and world trade
Purchasing Managers’ Surveys (diffusion indices; s.a.)
Composite Purchasing Managers’ Index
Global
2)
Merchandise
imports 1)
Global Purchasing Managers’ Index
United
United Japan China Memo item:
States Kingdom
euro area
Manufacturing
Services
2)
New export
orders
Global
Advanced
economies
Emerging
market
economies
1
2
3
4
5
6
7
8
9
10
11
12
2014
2015
2016
54.1
53.1
51.6
57.3
55.8
52.4
57.9
56.3
53.4
50.9
51.4
50.5
51.1
50.4
51.4
52.7
53.8
53.3
53.2
51.8
51.8
54.0
53.7
51.9
51.5
50.4
50.2
2.5
1.3
1.0
3.8
3.7
1.2
1.7
-0.3
0.8
2016 Q2
Q3
Q4
50.7
51.3
53.2
51.5
51.9
54.6
52.6
51.6
55.6
49.0
49.6
52.0
50.5
51.7
53.1
53.1
52.9
53.8
49.9
51.7
53.3
51.0
51.2
53.1
48.9
50.1
50.6
-0.1
0.8
1.3
0.3
0.9
-1.1
-0.3
0.7
2.9
2017 Q1
53.3
54.3
54.6
52.5
52.3
55.6
53.4
53.2
51.9
.
.
.
2016 Nov.
Dec.
53.0
53.5
54.9
54.1
55.3
56.7
52.0
52.8
52.9
53.5
53.9
54.4
53.2
53.5
53.0
53.5
50.6
50.7
0.5
1.3
-1.0
-1.1
1.5
2.9
2017 Jan.
Feb.
Mar.
Apr.
53.9
52.8
53.2
.
55.8
54.1
53.0
.
55.2
53.8
54.9
.
52.3
52.2
52.9
.
52.2
52.6
52.1
.
54.4
56.0
56.4
56.7
53.1
53.7
53.5
.
54.1
52.5
53.1
.
51.7
52.2
51.7
.
3.2
3.8
.
.
0.5
0.9
.
.
5.1
5.8
.
.
Sources: Markit (col. 1-9); CPB Netherlands Bureau for Economic Policy Analysis and ECB calculations (col. 10-12).
1) Global and advanced economies exclude the euro area. Annual and quarterly data are period-on-period percentages; monthly data are 3-month-on-3-month percentages. All data
are seasonally adjusted.
2) Excluding the euro area.
ECB Economic Bulletin, Issue 3 / 2017 - Statistics
S2
2 Financial developments
2.1 Money market interest rates
(percentages per annum; period averages)
Euro area 1)
Overnight
deposits
(EONIA)
1-month
deposits
(EURIBOR)
3-month
deposits
(EURIBOR)
6-month
deposits
(EURIBOR)
United States
Japan
3-month
deposits
(LIBOR)
3-month
deposits
(LIBOR)
12-month
deposits
(EURIBOR)
1
2
3
4
5
6
7
2014
2015
2016
0.09
-0.11
-0.32
0.13
-0.07
-0.34
0.21
-0.02
-0.26
0.31
0.05
-0.17
0.48
0.17
-0.03
0.23
0.32
0.74
0.13
0.09
-0.02
2016 Sep.
Oct.
Nov.
Dec.
-0.34
-0.35
-0.35
-0.35
-0.37
-0.37
-0.37
-0.37
-0.30
-0.31
-0.31
-0.32
-0.20
-0.21
-0.21
-0.22
-0.06
-0.07
-0.07
-0.08
0.85
0.88
0.91
0.98
-0.03
-0.02
-0.06
-0.04
2017 Jan.
Feb.
Mar.
-0.35
-0.35
-0.35
-0.37
-0.37
-0.37
-0.33
-0.33
-0.33
-0.24
-0.24
-0.24
-0.09
-0.11
-0.11
1.03
1.04
1.13
-0.02
-0.01
0.00
Source: ECB.
1) Data refer to the changing composition of the euro area, see the General Notes.
2.2 Yield curves
(End of period; rates in percentages per annum; spreads in percentage points)
Spot rates
Euro area
3 months
1 year
Spreads
Euro area
1), 2)
2 years
5 years
10 years
1), 2)
10 years
- 1 year
Instantaneous forward rates
United States United Kingdom
10 years
- 1 year
10 years
- 1 year
Euro area 1), 2)
1 year
2 years
5 years 10 years
1
2
3
4
5
6
7
8
9
10
11
12
2014
2015
2016
-0.02
-0.45
-0.93
-0.09
-0.40
-0.82
-0.12
-0.35
-0.80
0.07
0.02
-0.47
0.65
0.77
0.26
0.74
1.17
1.08
1.95
1.66
1.63
1.45
1.68
1.17
-0.15
-0.35
-0.78
-0.11
-0.22
-0.75
0.58
0.82
0.35
1.77
1.98
1.35
2016 Sep.
Oct.
Nov.
Dec.
-0.74
-0.82
-0.80
-0.93
-0.72
-0.74
-0.80
-0.82
-0.72
-0.66
-0.78
-0.80
-0.59
-0.38
-0.42
-0.47
-0.16
0.14
0.27
0.26
0.56
0.88
1.07
1.08
1.00
1.18
1.60
1.63
0.60
1.03
1.30
1.17
-0.71
-0.65
-0.80
-0.78
-0.71
-0.51
-0.69
-0.75
-0.22
0.17
0.39
0.35
0.64
1.03
1.29
1.35
2017 Jan.
Feb.
Mar.
-0.70
-0.87
-0.75
-0.70
-0.88
-0.74
-0.69
-0.90
-0.73
-0.28
-0.54
-0.36
0.50
0.25
0.38
1.20
1.13
1.12
1.69
1.56
1.36
1.36
1.05
1.01
-0.72
-0.92
-0.75
-0.60
-0.86
-0.64
0.64
0.34
0.47
1.63
1.46
1.52
United
States
Japan
Basic Consumer Consumer Oil and Financials Industrials Technology Utilities Telecoms Health care Standard
materials services
goods
gas
& Poor’s
500
Nikkei
225
Source: ECB.
1) Data refer to the changing composition of the euro area, see the General Notes.
2) ECB calculations based on underlying data provided by EuroMTS and ratings provided by Fitch Ratings.
2.3 Stock market indices
(index levels in points; period averages)
Dow Jones EURO STOXX indices
Benchmark
Main industry indices
Broad
index
50
1
2
3
4
5
6
7
8
9
10
11
12
2014
2015
2016
318.7 3,145.3
356.2 3,444.1
321.6 3,003.7
644.3
717.4
620.7
216.6
261.9
250.9
510.6
628.2
600.1
335.5
299.9
278.9
180.0
189.8
148.7
452.9
500.6
496.0
310.8
373.2
375.8
279.2
278.0
248.6
306.7
377.7
326.9
668.1
821.3
770.9
1,931.4 15,460.4
2,061.1 19,203.8
2,094.7 16,920.5
13
14
2016 Sep.
Oct.
Nov.
Dec.
325.5
327.9
324.5
342.6
3,012.1
3,042.3
3,026.4
3,207.3
635.6
649.8
654.4
698.1
255.4
253.5
247.7
253.7
617.6
620.8
594.1
619.1
281.3
291.0
286.0
313.6
142.8
146.7
152.5
165.7
518.7
519.1
515.1
541.6
396.1
393.0
378.7
396.0
251.6
247.2
231.5
237.1
321.0
318.4
306.9
320.9
780.1
768.8
778.3
797.3
2,157.7
2,143.0
2,165.0
2,246.6
2017 Jan. 352.4 3,298.8
Feb. 353.2 3,293.1
Mar. 365.7 3,427.1
720.9
728.9
740.4
258.4
257.0
261.7
637.7
644.9
671.6
321.1
312.5
314.2
170.1
166.6
174.7
557.7
563.0
578.4
412.7
431.7
450.3
240.1
239.1
252.1
337.5
334.6
349.6
817.4
839.5
870.0
2,275.1 19,194.1
2,329.9 19,188.7
2,366.8 19,340.2
16,737.0
17,044.5
17,689.5
19,066.0
Source: ECB.
ECB Economic Bulletin, Issue 3 / 2017 - Statistics
S3
2 Financial developments
2.4 MFI interest rates on loans to and deposits from households (new business) 1), 2)
(Percentages per annum; period average, unless otherwise indicated)
Deposits
Revolving Extended
loans
credit
Over- RedeemWith
and
card
night
able an agreed overdrafts
credit
at maturity of:
notice
of up Up to Over
to 3
2
2
months years years
Loans for consumption
Loans
Loans for house purchase
to sole
By initial period APRC 3) proprietors
By initial period
APRC 3) Composite
of rate fixation
and
of rate fixation
cost-ofunincorborrowing
Floating Over
porated Floating Over 1 Over 5 Over
indicator
rate and
1
partner- rate and and up and up
10
up to year
ships
up to
to 5
to 10 years
1 year
1 year
years
years
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
0.11
0.11
0.10
0.09
0.09
0.08
0.08
0.08
0.08
0.08
0.58
0.57
0.56
0.54
0.52
0.51
0.50
0.49
0.49
0.49
0.59
0.58
0.54
0.56
0.50
0.52
0.50
0.44
0.43
0.43
0.88
0.85
0.87
0.85
0.92
0.84
0.79
0.76
0.78
0.76
6.63
6.54
6.56
6.54
6.46
6.48
6.50
6.43
6.40
6.34
16.88
16.82
16.75
16.80
16.80
16.78
16.78
16.78
16.71
16.68
5.14
5.19
5.21
4.96
5.14
5.43
5.16
5.17
4.91
4.78
5.97
6.00
6.09
5.87
5.96
6.01
5.75
5.69
5.74
5.48
6.34
6.35
6.46
6.18
6.29
6.37
6.14
6.11
6.12
5.87
2.53
2.56
2.56
2.44
2.39
2.40
2.35
2.43
2.43
2.31
1.90
1.86
1.85
1.81
1.81
1.87
1.80
1.78
1.76
1.77
2.10
2.09
2.03
2.00
1.96
1.96
1.98
1.90
1.91
1.88
2.10
2.17
2.06
1.97
1.96
1.86
1.85
1.80
1.76
1.80
2.24
2.23
2.12
2.01
1.96
1.88
1.85
1.81
1.79
1.75
2.38
2.41
2.37
2.32
2.33
2.31
2.28
2.25
2.24
2.24
2.11
2.09
2.02
1.97
1.92
1.90
1.86
1.81
1.79
1.78
2017 Jan. 0.07
Feb. (p) 0.07
0.48
0.48
0.42
0.40
0.76
0.76
6.35
6.42
16.62
16.68
5.05
5.09
5.87
5.72
6.23
6.17
2.27
2.39
1.76
1.78
1.87
1.89
1.80
1.84
1.76
1.81
2.28
2.29
1.81
1.85
2016 Mar.
Apr.
May
June
July
Aug.
Sep.
Oct.
Nov.
Dec.
Source: ECB.
1) Data refer to the changing composition of the euro area.
2) Including non-profit institutions serving households.
3) Annual percentage rate of charge (APRC).
2.5 MFI interest rates on loans to and deposits from non-financial corporations (new business) 1), 2)
(Percentages per annum; period average, unless otherwise indicated)
Deposits
Revolving
loans and
Over- With an agreed overdrafts
night maturity of:
Other loans by size and initial period of rate fixation
up to EUR 0.25 million
Floating
Over
rate 3 months
and up to and up to
3 months
1 year
Up to Over
2 years 2 years
over EUR 0.25 and up to 1 million
Over
1 year
Floating
rate
and up to
3 months
Over
3 months
and up to
1 year
Over
1 year
Composite
cost-ofborrowing
indicator
over EUR 1 million
Floating
Over
rate 3 months
and up to and up to
3 months
1 year
Over
1 year
1
2
3
4
5
6
7
8
9
10
11
12
13
14
0.13
0.12
0.11
0.11
0.09
0.09
0.09
0.08
0.07
0.07
0.16
0.19
0.13
0.15
0.16
0.16
0.12
0.15
0.12
0.12
0.87
0.64
0.63
0.64
0.42
0.47
0.47
0.49
0.42
0.59
2.89
2.80
2.76
2.75
2.70
2.74
2.72
2.68
2.64
2.64
3.03
2.99
2.91
2.66
2.73
2.69
2.65
2.63
2.60
2.58
3.20
3.12
3.10
3.01
3.07
3.01
2.96
3.04
2.91
2.84
2.68
2.66
2.61
2.52
2.47
2.46
2.42
2.37
2.38
2.30
1.92
1.93
1.91
1.85
1.86
1.86
1.82
1.81
1.82
1.84
2.03
1.96
1.94
1.90
1.91
1.94
1.85
1.83
1.82
1.84
2.02
1.98
1.92
1.85
1.80
1.79
1.73
1.72
1.68
1.68
1.38
1.38
1.27
1.34
1.28
1.22
1.28
1.28
1.28
1.33
1.74
1.58
1.68
1.60
1.56
1.48
1.61
1.40
1.43
1.46
1.77
1.81
1.74
1.64
1.69
1.54
1.63
1.63
1.52
1.62
2.04
2.00
1.92
1.89
1.87
1.83
1.86
1.83
1.82
1.81
0.06
2017 Jan.
Feb. (p) 0.06
0.12
0.10
0.51
0.54
2.64
2.65
2.68
2.58
2.80
2.78
2.30
2.35
1.81
1.76
1.85
1.76
1.73
1.71
1.22
1.19
1.39
1.41
1.63
1.52
1.79
1.76
2016 Mar.
Apr.
May
June
July
Aug.
Sep.
Oct.
Nov.
Dec.
Source: ECB.
1) Data refer to the changing composition of the euro area.
2) In accordance with the ESA 2010, in December 2014 holding companies of non-financial groups were reclassified from the non-financial corporations sector to the financial
corporations sector.
ECB Economic Bulletin, Issue 3 / 2017 - Statistics
S4
2 Financial developments
2.6 Debt securities issued by euro area residents, by sector of the issuer and initial maturity
(EUR billions; transactions during the month and end-of-period outstanding amounts; nominal values)
Outstanding amounts
Total
Gross issues 1)
MFIs
Non-MFI corporations
General government Total
MFIs
Non-MFI corporations
General government
(including
(including
EuroFinancial
Non- Central
Other
EuroFinancial
NonCentral
Other
system) corporations
financial governgeneral
system) corporations
financial govern- general
other than FVCs corporations
ment
governother than FVCs corporations
ment governMFIs
ment
MFIs
ment
1
2
3
4
5
6
7
Short-term
8
9
10
11
12
13
14
2014
2015
2016
1,320
1,274
1,247
543
517
519
131
152
139
.
.
.
59
62
61
538
478
466
50
65
62
410
337
335
219
153
147
34
36
45
.
.
.
38
33
32
93
82
79
25
34
33
2016 Sep.
Oct.
Nov.
Dec.
1,310
1,296
1,310
1,247
539
529
536
519
145
145
152
139
.
.
.
.
69
71
70
61
492
484
487
466
66
67
65
62
354
341
349
305
159
155
139
128
44
45
63
69
.
.
.
.
30
35
33
33
86
69
88
50
36
37
26
25
2017 Jan. 1,277
Feb. 1,310
536
554
136
143
.
.
74
80
469
466
63
66
393
324
187
157
39
37
.
.
39
31
88
72
41
29
15,136
15,244
15,261
4,051
3,784
3,647
3,167
3,286
3,197
.
.
.
990
1,055
1,134
6,285
6,482
6,643
642
637
641
220
215
208
65
68
59
44
45
46
.
.
.
16
13
17
85
81
78
10
9
8
2016 Sep. 15,187
Oct. 15,217
Nov. 15,279
Dec. 15,261
3,678
3,674
3,667
3,647
3,142
3,170
3,177
3,197
.
.
.
.
1,098
1,104
1,130
1,134
6,631
6,628
6,664
6,643
638
641
641
641
217
239
216
163
52
56
43
45
46
61
64
77
.
.
.
.
29
22
26
13
84
92
76
25
7
8
7
2
2017 Jan. 15,317
Feb. 15,331
3,651
3,668
3,206
3,202
.
.
1,136
1,138
6,687
6,684
638
640
302
221
100
72
72
39
.
.
15
10
107
88
9
11
Long-term
2014
2015
2016
Source: ECB.
1) For the purpose of comparison, annual data refer to the average monthly figure over the year.
2.7 Growth rates and outstanding amounts of debt securities and listed shares
(EUR billions; percentage changes)
Debt securities
Total
MFIs
(including
Eurosystem)
Listed shares
Non-MFI corporations
Financial
corporations
other than
MFIs
General government
Nonfinancial
FVCs corporations
Total
MFIs
Financial
Noncorporations
financial
other than corporations
MFIs
Central
government
Other
general
government
5
6
Oustanding amount
7
8
9
10
11
1
2
3
4
2014
2015
2016
16,456.3
16,517.5
16,507.7
4,593.5
4,301.1
4,166.0
3,298.0
3,437.6
3,335.3
.
.
.
1,048.9
1,116.7
1,194.8
6,823.2
6,960.1
7,108.6
692.7
702.1
703.0
5,958.0
6,744.7
7,029.1
591.1
586.1
538.7
780.6
905.6
1,017.9
4,586.3
5,253.0
5,472.5
2016 Sep.
Oct.
Nov.
Dec.
16,496.8
16,513.4
16,589.3
16,507.7
4,216.8
4,203.6
4,203.5
4,166.0
3,286.8
3,315.1
3,329.4
3,335.3
.
.
.
.
1,167.0
1,175.5
1,199.8
1,194.8
7,122.4
7,111.9
7,150.4
7,108.6
703.9
707.3
706.1
703.0
6,593.0
6,665.7
6,651.0
7,029.1
427.5
479.2
482.3
538.7
872.2
907.7
952.8
1,017.9
5,293.3
5,278.8
5,215.9
5,472.5
2017 Jan.
Feb.
16,594.8
16,640.5
4,187.0
4,222.0
3,341.9
3,344.6
.
.
1,209.7
1,218.0
7,155.7
7,149.7
700.5
706.1
7,015.2
7,199.0
542.3
539.0
1,016.0
1,024.3
5,456.9
5,635.7
2014
2015
2016
-0.7
0.3
0.1
-8.1
-7.0
-2.9
0.4
5.6
-2.3
.
.
.
4.9
4.7
7.1
3.1
1.8
2.1
1.1
0.6
-0.1
1.6
1.1
0.5
7.2
4.5
1.2
2.0
1.5
1.0
0.7
0.6
0.4
2016 Sep.
Oct.
Nov.
Dec.
0.0
-0.2
-0.1
0.1
-3.8
-4.0
-4.2
-2.9
-0.8
-1.4
-0.9
-2.3
.
.
.
.
5.6
6.3
7.2
7.1
1.6
1.5
1.6
2.1
1.9
1.6
-0.5
-0.1
0.9
0.9
0.8
0.5
2.8
2.8
2.8
1.2
1.7
1.4
1.0
1.0
0.6
0.7
0.5
0.4
2017 Jan.
Feb.
0.7
1.1
-2.0
-1.6
-1.2
0.7
.
.
8.9
9.8
2.1
1.6
-0.3
0.7
0.6
0.7
1.5
4.1
1.1
1.1
0.4
0.3
Growth rate
Source: ECB.
ECB Economic Bulletin, Issue 3 / 2017 - Statistics
S5
2 Financial developments
2.8 Effective exchange rates 1)
(period averages; index: 1999 Q1=100)
EER-19
Nominal
Real CPI
Real PPI
EER-38
Real GDP
deflator
Real ULCM 2)
Real ULCT
Nominal
Real CPI
1
2
3
4
5
6
7
8
101.8
92.4
94.8
97.8
88.4
90.1
97.0
89.3
91.4
91.9
83.6
85.7
98.3
82.7
81.8
100.0
89.6
90.6
114.7
106.5
110.4
96.1
87.8
90.0
2016 Q2
Q3
Q4
94.9
95.2
94.9
90.3
90.5
90.2
91.7
91.7
91.1
85.9
86.0
85.6
81.9
81.5
81.6
90.8
90.6
90.3
110.8
110.6
110.0
90.4
90.1
89.6
2014
2015
2016
2017 Q1
94.2
89.6
90.3
2016 Oct.
Nov.
Dec.
95.5
95.0
94.2
90.8
90.2
89.6
91.8
91.1
90.4
2017 Jan.
Feb.
Mar.
94.4
93.9
94.4
89.7
89.3
89.8
2017 Mar.
0.6
0.5
2017 Mar.
0.4
0.3
.
109.2
88.7
-
.
-
110.6
110.3
109.2
90.1
89.7
88.9
90.4
89.9
90.4
Percentage change versus previous month
-
-
109.7
108.8
109.2
89.1
88.3
88.6
0.5
Percentage change versus previous year
-
-
0.4
0.3
-
-
-0.7
-1.4
-
-0.6
-
.
Source: ECB.
1) For a definition of the trading partner groups and other information see the General Notes to the Statistics Bulletin.
2) ULCM-deflated series are available only for the EER-18 trading partner group.
2.9 Bilateral exchange rates
(period averages; units of national currency per euro)
Chinese
renminbi
Croatian
kuna
Czech
koruna
Danish Hungarian Japanese
krone
forint
yen
Polish
zloty
Pound Romanian
sterling
leu
Swedish
krona
Swiss
franc
US
Dollar
1
2
3
4
5
6
7
8
9
10
11
12
2014
2015
2016
8.186
6.973
7.352
7.634
7.614
7.533
27.536
27.279
27.034
7.455
7.459
7.445
308.706
309.996
311.438
140.306
134.314
120.197
4.184
4.184
4.363
0.806
0.726
0.819
4.4437
4.4454
4.4904
9.099
9.353
9.469
1.215
1.068
1.090
1.329
1.110
1.107
2016 Q2
Q3
Q4
7.379
7.443
7.369
7.504
7.493
7.523
27.040
27.029
27.029
7.439
7.442
7.439
313.371
311.016
309.342
121.949
114.292
117.918
4.372
4.338
4.378
0.787
0.850
0.869
4.4986
4.4646
4.5069
9.278
9.511
9.757
1.096
1.089
1.080
1.129
1.117
1.079
2017 Q1
7.335
7.467
27.021
7.435
309.095
121.014
4.321
0.860
4.5217
9.506
1.069
1.065
2016 Oct.
Nov.
Dec.
7.420
7.388
7.298
7.507
7.521
7.540
27.022
27.033
27.031
7.440
7.441
7.436
307.000
308.816
312.235
114.473
116.933
122.395
4.308
4.391
4.436
0.894
0.869
0.844
4.4942
4.5100
4.5164
9.707
9.851
9.709
1.089
1.076
1.075
1.103
1.080
1.054
2017 Jan.
Feb.
Mar.
7.319
7.314
7.369
7.530
7.448
7.423
27.021
27.021
27.021
7.435 308.987 122.136
4.367
7.435 308.502 120.168
4.308
7.436 309.714 120.676
4.287
Percentage change versus previous month
0.861
0.853
0.866
4.5018
4.5136
4.5476
9.511
9.476
9.528
1.071
1.066
1.071
1.061
1.064
1.068
2017 Mar.
0.8
-0.3
0.0
1.5
0.8
0.5
0.4
0.4
2017 Mar.
2.0
-1.8
-0.1
10.9
1.8
2.6
-2.0
-3.7
0.0
0.4
0.4
-0.5
Percentage change versus previous year
-0.3
-0.5
-3.8
-0.1
Source: ECB.
ECB Economic Bulletin, Issue 3 / 2017 - Statistics
S6
2 Financial developments
2.10 Euro area balance of payments, financial account
(EUR billions, unless otherwise indicated; outstanding amounts at end of period; transactions during period)
Total 1)
Direct
investment
Assets Liabilities
Net
1
2
2016 Q1
Q2
Q3
Q4
22,214.0
22,791.7
23,035.1
23,577.7
23,223.8
23,620.8
23,792.8
24,207.2
-1,009.7
-829.1
-757.6
-629.5
2016 Q4
219.5
225.4
-5.9
Portfolio
investment
Assets Liabilities
Assets
Net
financial
derivatives
Liabilities
8,038.3
8,256.7
8,116.4
8,397.5
7,112.1
7,429.5
7,691.7
7,885.1
Reserve
assets
Memo:
Gross
external
debt
Assets
Liabilities
8
9
10
11
12
-29.2
-62.1
-62.5
-55.1
4,738.4
4,829.9
4,836.0
4,802.6
5,239.2
5,418.2
5,545.7
5,524.1
675.3
721.8
727.0
707.7
13,420.1
13,576.8
13,576.9
13,558.8
95.8
-0.5
44.7
51.4
6.6
126.2
3
4
5
6
7
Outstanding amounts (international investment position)
9,717.4
9,872.6
9,842.9
10,237.5
Other investment
9,946.3
9,945.9
10,130.7
10,285.6
Outstanding amounts as a percentage of GDP
95.3
78.2
73.4
Transactions
2016 Q1
Q2
Q3
Q4
409.5
236.6
196.2
123.7
359.8
150.0
75.9
27.5
49.6
86.6
120.3
96.3
124.8
16.8
39.3
145.9
74.5
50.2
-75.8
112.5
132.4
122.4
127.2
13.4
28.3
-72.0
5.7
-60.9
29.0
-45.8
23.8
15.4
122.3
141.1
-1.8
-55.4
257.0
171.8
146.0
-24.2
1.0
2.2
7.7
4.6
-
2016 Sep.
Oct.
Nov.
Dec.
-70.3
261.6
25.4
-163.3
-126.8
253.7
24.6
-250.7
56.5
8.0
0.8
87.4
-5.5
87.0
28.8
30.1
-56.0
52.8
51.1
8.6
11.7
5.1
-14.5
22.7
5.5
-46.4
15.4
-29.9
3.7
6.2
2.9
6.3
-86.9
167.2
5.8
-228.5
-76.3
247.2
-42.0
-229.4
6.8
-4.0
2.5
6.1
-
2017 Jan.
Feb.
379.1
189.8
367.4
193.0
11.8
-3.2
108.2
91.3
43.6
31.2
95.1
95.3
53.8
-19.3
12-month cumulated transactions
2.3
5.0
230.2
34.0
244.9
117.0
-5.1
2.0
-
2017 Feb.
1,137.1
786.2
350.9
438.4
285.0
409.7
-58.5
0.9
12-month cumulated transactions as a percentage of GDP
275.6
559.7
12.5
-
2017 Feb.
10.6
7.3
2.6
5.2
0.1
-
3.3
4.1
2.7
3.8
-0.5
0.0
Source: ECB.
1) Net financial derivatives are included in total assets.
ECB Economic Bulletin, Issue 3 / 2017 - Statistics
S7
3 Economic activity
3.1 GDP and expenditure components
(quarterly data seasonally adjusted; annual data unadjusted)
GDP
Total
Domestic demand
Total
1
2014
2015
2016
2016 Q1
Q2
Q3
Q4
2016
Private Government
consumption consumption
2
3
10,135.9 9,776.3
10,460.7 9,986.7
10,740.9 10,259.0
5,632.2
5,743.3
5,877.0
4
External balance 1)
Gross fixed capital formation
Changes in
inventories 2)
Total
Exports 1)
Imports 1)
Total
Total
construction machinery
Intellectual
property
products
5
6
7
Current prices (EUR billions)
8
9
10
11
12
600.0
633.3
660.9
383.1
408.4
439.7
30.2
12.8
-0.9
359.5
474.0
481.9
4,532.9
4,831.7
4,905.4
4,173.3
4,357.7
4,423.5
103.2
109.2
104.1
122.2
1.5
-2.3
2.1
6.0
126.8
123.1
124.7
103.9
1,200.1
1,215.4
1,224.3
1,258.9
1,073.3
1,092.3
1,099.5
1,155.0
4.1
0.0
4.5
-
-
1.5
5.9
-5.0
17.5
-
-
0.3
1.3
0.4
1.8
-0.1
1.5
0.1
3.9
3.4
5.6
7.0
-
-
4.4
6.5
2.9
4.9
6.5
4.0
-
-
2.4
2.5
2.7
3.8
3.4
4.1
2.9
5.5
-0.3
-0.1
0.1
0.3
0.1
0.0
0.1
-0.8
-
-
2,125.5 1,988.4
2,164.7 2,066.0
2,221.7 2,161.2
1,000.4
1,019.2
1,055.4
2,660.8
2,672.0
2,688.5
2,712.5
2,534.0
2,548.8
2,563.8
2,608.7
1,454.6
1,462.9
1,469.8
1,484.7
551.3
553.7
556.4
560.2
526.6
534.4
535.5
557.8
259.7
162.4
260.3
163.6
264.6
165.4
267.4
166.9
as a percentage of GDP
100.0
95.5
54.7
20.7
20.1
9.8
6.2
Chain-linked volumes (prices for the previous year)
quarter-on-quarter percentage changes
2016 Q1
Q2
Q3
Q4
0.6
0.3
0.4
0.5
0.4
0.4
0.3
1.4
0.7
0.4
0.3
0.5
0.7
0.3
0.1
0.5
0.8
0.9
0.1
1.1
-0.6
1.0
-0.2
1.5
0.4
3.3
0.1
-0.4
annual percentage changes
2014
2015
2016
1.2
2.0
1.8
1.2
1.9
2.2
0.8
1.8
2.0
0.6
1.3
1.9
2016 Q1
Q2
Q3
Q4
1.7
1.6
1.8
1.8
2.1
2.3
1.8
2.5
2016 Q1
Q2
Q3
Q4
0.6
0.3
0.4
0.5
0.4
0.3
0.3
1.3
0.4
0.2
0.2
0.2
2014
2015
2016
1.2
2.0
1.8
1.2
1.8
2.1
0.4
1.0
1.1
0.1
0.3
0.4
0.3
0.6
0.7
-0.1
0.1
0.2
0.3
0.3
0.2
0.1
0.2
0.3
0.3
-0.1
-0.1
0.0
0.2
-0.3
-
-
2016 Q1
Q2
Q3
Q4
1.7
1.6
1.8
1.8
2.0
2.1
1.8
2.4
1.1
1.0
1.0
1.0
0.4
0.4
0.4
0.3
0.5
0.7
0.5
1.0
0.2
0.2
0.3
0.2
0.2
0.3
0.2
0.1
0.1
0.2
0.0
0.8
0.0
0.0
0.0
0.0
-0.3
-0.5
0.0
-0.6
-
-
1.5
3.2
3.7
-0.9
1.4
2.4
4.6
4.7
3.8
2.0
2.0
2.5
2.0
4.1
1.4
1.9
2.1
3.8
1.9
5.2
6.2
1.8
1.7
2.4
2.6
3.9
-0.2
1.9
1.6
5.1
1.9
1.1
19.9
contributions to quarter-on-quarter percentage changes in GDP; percentage points
0.1
0.2
0.1
0.0
0.1
0.1
0.2
-0.1
0.1
0.2
0.0
0.0
0.1
0.0
-0.2
0.1
0.7
0.0
0.0
0.7
contributions to annual percentage changes in GDP; percentage points
Sources: Eurostat and ECB calculations.
1) Exports and imports cover goods and services and include cross-border intra-euro area trade.
2) Including acquisitions less disposals of valuables.
ECB Economic Bulletin, Issue 3 / 2017 - Statistics
S8
3 Economic activity
3.2 Value added by economic activity
(quarterly data seasonally adjusted; annual data unadjusted)
Gross value added (basic prices)
Total
Agriculture, Manufacturing
forestry and
energy and
fishing
utilities
Construction
1
2
3
4
2014
2015
2016
9,101.6
9,388.9
9,632.6
150.0
150.6
145.7
1,777.3
1,886.9
1,925.4
461.1
466.7
486.5
2016 Q1
Q2
Q3
Q4
2,387.2
2,396.2
2,410.8
2,431.5
36.1
35.9
36.2
37.3
478.8
476.9
480.9
486.0
120.2
120.8
121.7
123.3
100.0
1.5
20.0
2016
5.1
Trade,
Infor- Finance
Real Professional,
transport, mation
and estate business and
accom- and com- insurance
support
modation municaservices
and food
tion
services
5
6
7
Current prices (EUR billions)
Public administration,
education,
health and
social work
Arts, entertainment
and other
services
Taxes less
subsidies
on
products
8
9
10
11
12
461.5 1,044.9
459.8 1,062.8
452.5 1,090.6
979.3
1,022.0
1,064.6
1,778.8
1,817.7
1,865.7
321.4
326.8
336.5
1,034.3
1,071.8
1,108.3
450.1
109.8
113.8 269.8
452.8
110.8
113.0 271.8
455.2
111.4
113.0 273.3
460.5
112.3
112.6 275.5
as a percentage of value added
262.7
265.4
267.1
268.9
462.3
465.0
467.7
470.4
83.5
83.9
84.2
84.7
273.6
275.7
277.7
281.1
11.1
19.4
3.5
-
0.3
0.3
0.2
0.3
0.8
1.1
0.4
0.3
0.4
0.2
0.3
0.2
0.5
0.2
0.3
0.3
0.1
0.5
0.6
0.6
0.5
0.8
1.0
2.5
2.9
3.0
0.5
1.0
1.1
0.1
0.0
1.3
1.1
3.2
2.5
1,711.3
1,766.5
1,820.5
18.9
415.8
429.1
444.6
4.6
4.7
11.3
Chain-linked volumes (prices for the previous year)
quarter-on-quarter percentage changes
2016 Q1
Q2
Q3
Q4
0.6
0.3
0.4
0.5
-1.2
-0.8
-0.7
0.1
0.4
0.1
0.7
0.7
1.1
0.0
0.4
0.8
2014
2015
2016
1.2
1.9
1.7
1.2
-0.6
-2.0
2.4
4.3
1.5
-1.1
-0.1
1.9
2016 Q1
Q2
Q3
Q4
1.5
1.6
1.7
1.8
-1.8
-1.8
-2.2
-2.6
1.7
1.4
2.3
2.5
0.3
0.8
2.7
1.2
1.5
2.3
3.0
-0.3
1.0
3.3
1.2
2.4
2.3
3.6
0.5
1.0
3.1
2.0
2.3
2.5
4.0
0.4
1.1
2.6
contributions to quarter-on-quarter percentage changes in value added; percentage points
0.9
1.0
1.2
1.1
0.9
1.3
1.4
1.4
3.3
2.4
2.6
1.8
2016 Q1
Q2
Q3
Q4
0.6
0.3
0.4
0.5
0.0
0.0
0.0
0.0
0.1
0.1
0.2
0.0
0.0
0.0
0.1
0.0
0.0
0.1
0.1
0.0
0.0
0.1
0.1
0.0
0.1
0.1
0.0
0.0
0.0
0.1
0.0
0.1
0.0
0.0
0.0
0.0
contributions to annual percentage changes in value added; percentage points
0.1
0.0
0.1
0.0
0.0
0.0
0.0
0.0
-
2014
2015
2016
1.2
1.9
1.7
0.0
0.0
0.0
0.5
0.8
0.3
-0.1
0.0
0.1
0.2
0.4
0.5
0.2
0.1
0.2
-0.1
0.0
0.0
0.1
0.1
0.1
0.3
0.3
0.3
0.1
0.2
0.2
0.0
0.0
0.0
-
2016 Q1
Q2
Q3
Q4
1.5
1.6
1.7
1.8
0.0
0.0
0.0
0.0
0.3
0.2
0.2
0.4
0.1
0.1
0.1
0.1
0.4
0.4
0.4
0.5
0.1
0.1
0.2
0.2
0.0
0.0
0.0
0.0
0.1
0.1
0.1
0.1
0.3
0.4
0.3
0.3
0.2
0.2
0.2
0.2
0.0
0.0
0.0
0.0
-
1.0
0.9
1.0
0.4
1.2
-0.6
0.4
1.3
0.0
0.7
0.5
0.0
annual percentage changes
1.2
2.1
2.4
3.5
2.9
3.3
-1.2
-0.4
0.3
Sources: Eurostat and ECB calculations.
ECB Economic Bulletin, Issue 3 / 2017 - Statistics
S9
3 Economic activity
3.3 Employment 1)
(quarterly data seasonally adjusted; annual data unadjusted)
Total
By employment
status
By economic activity
EmploySelfees employed
Agricul- Manufacture,
turing,
forestry
energy
and
and
fishing
utilities
Construction
6
Trade,
Infor- Finance
Real Professional,
transport, mation
and estate business and
accomand
insursupport
modation
comance
services
and food municaservices
tion
1
2
3
4
5
7
8
Persons employed
2014
2015
2016
100.0
100.0
100.0
85.0
85.2
85.4
15.0
14.8
14.6
3.4
3.3
3.2
15.1
14.9
14.8
2014
2015
2016
0.6
1.0
1.3
0.6
1.2
1.5
0.1
0.0
-0.2
0.0
-1.0
-0.5
-0.4
0.2
0.5
-1.7
0.1
0.0
0.7
1.2
1.7
2016 Q1
Q2
Q3
Q4
1.4
1.4
1.2
1.2
1.7
1.7
1.5
1.3
-0.6
-0.2
0.0
0.3
-1.4
-0.8
-0.1
0.2
0.7
0.6
0.5
0.4
0.0
-0.3
0.0
0.5
1.7
2.0
1.7
1.6
9
Public adminisArts,
tration, edu- entertainment
cation, health
and other
and
services
social work
10
11
12
13
2.7
2.6
2.6
1.0
1.0
1.0
13.1
13.3
13.5
24.2
24.1
24.1
7.1
7.1
7.0
0.6
1.2
2.2
-0.8
-0.4
-0.1
0.8
1.8
1.6
2.1
3.1
2.8
1.0
1.0
1.2
0.5
0.9
0.8
2.2
2.0
2.0
2.4
-0.2
0.1
0.0
-0.2
1.6
1.2
1.9
1.7
3.3
2.9
2.7
2.5
1.2
1.3
1.2
1.0
1.5
1.1
0.5
0.2
2.7
2.7
2.6
1.0
1.0
1.0
12.8
13.0
13.2
22.0
21.9
21.9
6.3
6.3
6.3
as a percentage of total persons employed
6.1
24.8
2.7
6.0
24.8
2.7
6.0
24.9
2.8
annual percentage changes
Hours worked
as a percentage of total hours worked
2014
2015
2016
100.0
100.0
100.0
80.3
80.5
80.7
19.7
19.5
19.3
4.4
4.3
4.3
15.6
15.5
15.4
6.8
25.7
2.9
6.8
25.6
2.9
6.7
25.8
2.9
annual percentage changes
2014
2015
2016
0.5
1.2
1.1
0.8
1.4
1.4
-0.5
0.0
0.1
-0.5
0.0
-0.2
0.0
0.6
0.6
-1.4
0.7
-0.1
0.4
0.9
1.6
0.6
2.3
2.0
-0.9
-0.3
0.1
0.6
2.2
1.4
2.2
3.2
2.8
1.1
1.1
0.8
0.2
0.9
0.6
2016 Q1
Q2
Q3
Q4
1.5
1.5
1.0
0.9
1.8
1.6
1.2
1.1
-0.1
0.9
-0.1
0.0
0.0
0.1
-0.3
-0.3
1.0
0.9
0.4
0.5
0.3
0.0
-0.2
-0.5
1.7
2.1
1.7
1.2
2.5
2.3
1.6
1.8
0.0
0.8
0.0
-0.3
1.5
1.9
1.4
1.1
3.8
3.4
2.2
2.0
0.9
0.9
0.6
0.9
1.2
0.9
-0.1
-0.1
Hours worked per person employed
annual percentage changes
2014
2015
2016
0.0
0.1
-0.1
0.1
0.2
-0.1
-0.6
0.0
0.3
-0.6
1.0
0.3
0.4
0.4
0.1
0.3
0.7
-0.2
-0.3
-0.2
-0.1
0.0
1.1
-0.2
-0.1
0.1
0.2
-0.2
0.4
-0.1
0.1
0.1
0.0
0.1
0.1
-0.3
-0.3
0.0
-0.2
2016 Q1
Q2
Q3
Q4
0.1
0.1
-0.3
-0.3
0.1
-0.1
-0.2
-0.2
0.6
1.1
-0.1
-0.2
1.5
0.9
-0.2
-0.5
0.3
0.3
-0.1
0.2
0.3
0.3
-0.3
-0.9
0.0
0.1
0.0
-0.4
0.3
0.2
-0.4
-0.5
0.2
0.7
0.0
-0.1
0.0
0.7
-0.5
-0.6
0.4
0.5
-0.5
-0.4
-0.3
-0.4
-0.5
-0.1
-0.3
-0.2
-0.7
-0.3
Sources: Eurostat and ECB calculations.
1) Data for employment are based on the ESA 2010.
ECB Economic Bulletin, Issue 3 / 2017 - Statistics
S 10
3 Economic activity
3.4 Labour force, unemployment and job vacancies
(seasonally adjusted, unless otherwise indicated)
Labour
force,
millions 1)
1
UnderUnemployment
Job
employvacancy
ment,
Total
Long-term
By age
By gender
rate 2)
% of
unemploylabour Millions % of
ment,
Adult
Youth
Male
Female
force 1)
labour
% of
force
labour Millions
% of Millions
% of Millions
% of Millions
% of % of total
force 1)
labour
labour
labour
labour
posts
force
force
force
force
2
3
% of total
in 2016
4
5
6
100.0
7
8
81.8
9
18.2
10
11
52.2
12
13
14
47.8
2014
2015
2016
160.334
160.600
161.974
4.6
4.6
4.3
18.635
17.441
16.226
11.6
10.9
10.0
6.1
5.6
5.0
15.215
14.292
13.275
10.4
9.8
9.0
3.420
3.149
2.951
23.7
22.3
20.9
9.929
9.252
8.471
11.5
10.7
9.7
8.706
8.189
7.755
11.8
11.0
10.4
1.5
1.5
1.7
2016 Q1
Q2
Q3
Q4
161.014
161.849
162.465
162.570
4.5
4.5
4.1
4.2
16.629
16.424
16.082
15.767
10.3
10.1
9.9
9.7
5.2
5.1
4.8
4.9
13.616
13.432
13.164
12.887
9.2
9.1
8.9
8.7
3.012
2.992
2.919
2.880
21.5
21.1
20.6
20.4
8.713
8.530
8.381
8.261
10.0
9.8
9.6
9.4
7.916
7.894
7.702
7.506
10.6
10.5
10.3
10.0
1.7
1.7
1.6
1.7
2016 Sep.
Oct.
Nov.
Dec.
-
-
15.995
15.839
15.807
15.656
9.9
9.8
9.7
9.6
-
13.104
12.967
12.895
12.799
8.8
8.7
8.7
8.6
2.891
2.871
2.912
2.856
20.4
20.3
20.6
20.2
8.352
8.301
8.299
8.182
9.6
9.5
9.5
9.4
7.643
7.538
7.508
7.473
10.2
10.1
10.0
10.0
-
2017 Jan.
Feb.
-
-
15.579
15.439
9.6
9.5
-
12.791
12.717
8.6
8.6
2.788
2.722
19.8
19.4
8.130
8.053
9.3
9.2
7.449
7.386
9.9
9.8
-
Sources: Eurostat and ECB calculations.
1) Not seasonally adjusted.
2) The job vacancy rate is equal to the number of job vacancies divided by the sum of the number of occupied posts and the number of job vacancies, expressed as a percentage.
3.5 Short-term business statistics
Industrial production
Total
(excluding construction)
Manufacturing
Con- ECB indicator
struction on industrial
producnew orders
tion
Main Industrial Groupings
Intermediate
goods
Total
Food, Non-food
beverages,
tobacco
New
passenger
Fuel car registrations
Capital Consumer Energy
goods
goods
1
2
3
4
5
6
7
100.0
86.0
33.6
29.2
22.5
14.7
100.0
2014
2015
2016
0.8
2.1
1.4
1.7
2.3
1.5
1.1
1.0
1.8
1.8
3.6
1.7
2.6
2.5
1.1
-5.4
0.8
0.0
2.0
-0.9
1.9
3.1
3.6
0.3
2016 Q2
Q3
Q4
1.1
1.0
2.3
1.1
1.2
1.8
1.2
1.6
2.4
1.3
0.9
1.7
1.0
1.2
1.1
-0.7
-0.5
5.3
-0.1
3.1
2.2
-2.4
-0.2
3.3
% of total
in 2010
Retail sales
9
10
11
12
13
100.0 100.0
8
39.3
51.5
9.1
100.0
1.5
2.7
1.9
0.7
1.7
1.3
2.4
3.6
2.5
0.0
2.3
1.8
3.8
8.8
7.2
1.8
1.3
2.2
0.6
1.3
1.6
2.7
1.4
3.0
2.2
2.4
1.3
8.5
6.4
4.1
annual percentage changes
.
.
.
.
.
.
.
.
.
.
.
.
4.8
2016 Oct.
Nov.
Dec.
0.8
3.3
2.7
0.5
2.9
1.9
0.9
3.0
3.7
1.2
3.3
0.5
-0.7
2.4
1.8
2.0
6.2
7.4
1.6
0.8
3.3
2.6
2.4
4.9
2.9
2.5
1.3
2.3
1.7
0.8
3.8
3.5
1.9
1.4
2.1
0.4
4.2
4.5
3.4
2017 Jan.
Feb.
Mar.
0.2
1.2
.
-0.8
0.9
.
0.7
2.0
.
-1.7
1.2
.
-2.5
-1.9
.
7.8
2.4
.
-5.1
7.1
.
3.0
5.5
.
1.5
1.8
.
1.1
0.8
.
1.9
2.4
.
1.5
0.5
.
3.7
4.8
5.5
2016 Oct.
Nov.
Dec.
0.2
1.5
-1.1
0.0
1.6
-1.1
-0.2
2.0
-0.1
1.3
0.3
-2.7
-0.8
1.7
0.1
1.0
1.2
-1.1
0.5
1.0
-0.3
1.9
0.5
3.1
1.2
-0.2
-0.3
0.2
-0.5
0.0
2.5
-0.2
-0.3
-0.7
0.4
-0.3
-3.7
2.4
2.2
2017 Jan.
Feb.
Mar.
0.3
-0.3
.
0.1
0.2
.
-0.8
1.0
.
1.6
0.9
.
-1.1
-1.0
.
2.0
-4.7
.
-2.4
6.9
.
-2.8
1.4
.
0.1
0.7
.
-0.1
0.7
.
0.0
0.9
.
1.2
-0.9
.
0.8
0.7
-0.3
2017 Q1
month-on-month percentage changes (s.a.)
Sources: Eurostat, ECB calculations, ECB experimental statistics (col. 8) and European Automobile Manufacturers Association (col. 13).
ECB Economic Bulletin, Issue 3 / 2017 - Statistics
S 11
3 Economic activity
3.6 Opinion surveys
(seasonally adjusted)
European Commission Business and Consumer Surveys
(percentage balances, unless otherwise indicated)
Purchasing Managers’ Surveys
(diffusion indices)
Economic Manufacturing industry Consumer Construction
Retail
Service industries
Purchasing
sentiment
confidence
confidence
trade
Managers’
indicator
Industrial Capacity
indicator
indicator confidServices
Capacity Index (PMI)
(long-term confidence utilisation
ence confidence
utilisation for manuaverage
indicator
(%)
indicator
indicator
(%)
facturing
= 100)
8
Manu- Business Composite
facturing
activity
output
output
for
services
1
2
3
4
5
6
7
9
10
11
12
1999-13
100.0
-6.1
80.7
-12.8
-13.6
-8.7
7.0
-
51.0
52.4
52.9
52.7
2014
2015
2016
101.4
104.2
104.8
-3.8
-3.1
-2.6
80.5
81.4
81.9
-10.2
-6.2
-7.7
-26.6
-22.4
-16.6
-3.1
1.6
1.5
4.9
9.3
11.2
87.7
88.4
89.1
51.8
52.2
52.5
53.3
53.4
53.6
52.5
54.0
53.1
52.7
53.8
53.3
2016 Q2
Q3
Q4
104.2
104.2
106.9
-3.4
-2.9
-0.6
81.6
82.0
82.4
-7.8
-8.2
-6.4
-18.4
-16.0
-13.1
1.8
0.3
1.8
11.2
10.3
12.4
89.0
89.2
89.4
52.0
52.1
54.0
53.0
53.7
54.9
53.1
52.6
53.5
53.1
52.9
53.8
2017 Q1
107.9
1.1
.
-5.4
-11.0
2.0
13.1
.
55.6
56.9
55.1
55.6
2016 Nov.
Dec.
106.5
107.8
-1.1
0.0
-
-6.2
-5.1
-12.9
-12.1
1.5
3.5
12.2
12.9
-
53.7
54.9
54.1
56.1
53.8
53.7
53.9
54.4
2017 Jan.
Feb.
Mar.
Apr.
107.9
108.0
107.9
.
0.8
1.3
1.2
.
82.5
-
-4.8
-6.2
-5.0
-3.6
-12.9
-10.1
-9.9
.
2.3
1.8
1.8
.
12.8
13.9
12.7
.
89.4
-
55.2
55.4
56.2
56.8
56.1
57.3
57.5
58.0
53.7
55.5
56.0
56.2
54.4
56.0
56.4
56.7
Sources: European Commission (Directorate-General for Economic and Financial Affairs) (col. 1-8) and Markit (col. 9-12).
3.7 Summary accounts for households and non-financial corporations
(current prices, unless otherwise indicated; not seasonally adjusted)
Households
Non-financial corporations
Saving Debt Real gross
Financial Non-financial
Net Housratio ratio disposable investment
investment worth
ing
2)
income
(gross)
wealth
(gross) 1)
Percentage of
gross disposable
income (adjusted)
Profit
share 3)
Saving
ratio
(net)
Percentage of net
value added
Annual percentage changes
Debt
Financial Non-financial
ratio 4) investment
investment
(gross)
Percentage of
GDP
Financing
Annual percentage changes
1
2
3
4
5
6
7
8
9
10
11
12
13
2013
2014
2015
12.5
12.5
12.3
95.6
94.7
94.1
-0.5
0.7
1.8
1.1
1.8
2.1
-5.0
1.0
2.5
1.0
2.7
3.2
-1.2
1.0
2.3
32.6
33.0
34.5
4.4
4.9
6.6
130.0
131.2
133.6
2.2
2.5
3.9
-0.4
6.8
2.8
0.8
1.4
2.1
2016 Q1
Q2
Q3
Q4
12.3
12.5
12.5
.
93.5
93.6
93.5
.
2.4
2.4
1.6
1.1
2.0
2.4
2.3
2.1
3.9
6.6
5.7
5.6
2.0
3.2
4.2
4.2
3.1
3.6
3.9
4.4
34.1
33.9
34.0
33.9
6.8
7.0
7.3
7.4
132.7
133.9
132.6
133.1
3.6
3.7
3.6
3.3
4.3
3.8
3.8
8.0
1.9
2.0
1.9
1.8
Sources: ECB and Eurostat.
1) Based on four-quarter cumulated sums of both saving and gross disposable income (adjusted for the change in the net equity of households in pension fund reserves).
2) Financial assets (net of financial liabilities) and non-financial assets. Non-financial assets consist mainly of housing wealth (residential structures and land). They also include
non-financial assets of unincorporated enterprises classified within the household sector.
3) The profit share uses net entrepreneurial income, which is broadly equivalent to current profits in business accounting.
4) Based on the outstanding amount of loans, debt securities, trade credits and pension scheme liabilities.
ECB Economic Bulletin, Issue 3 / 2017 - Statistics
S 12
3 Economic activity
3.8 Euro area balance of payments, current and capital accounts
(EUR billions; seasonally adjusted unless otherwise indicated; transactions)
Current account
Total
Credit
Goods
Debit
Net
Credit
Capital
account 1)
Services
Debit
Credit
Primary income
Debit
Credit
Secondary income
Debit
Credit
Debit
Credit
Debit
1
2
3
4
5
6
7
8
9
10
11
12
13
2016 Q1
Q2
Q3
Q4
887.3
895.0
902.5
935.2
793.3
793.4
812.8
861.1
94.0
101.6
89.6
74.1
516.1
519.9
524.3
542.4
424.1
421.1
431.4
453.3
196.8
193.0
197.0
199.8
177.2
178.1
177.6
204.8
148.6
155.7
154.6
165.2
135.1
139.1
136.4
138.7
25.8
26.5
26.7
27.9
57.0
55.1
67.4
64.4
9.4
7.1
6.6
9.5
11.0
7.3
5.5
10.1
2016 Sep.
Oct.
Nov.
Dec.
303.5
307.4
315.6
312.2
271.9
284.9
288.4
287.8
31.6
22.5
27.3
24.4
176.4
176.9
181.8
183.7
143.6
149.1
151.6
152.6
66.4
67.0
66.6
66.2
59.8
68.3
69.0
67.5
51.5
54.5
58.4
52.3
45.7
45.6
45.9
47.1
9.1
9.0
8.9
10.1
22.8
21.9
21.9
20.6
2.4
1.9
2.3
5.3
1.9
2.7
2.8
4.6
2017 Jan.
Feb.
313.0
314.2
286.8
276.3
26.1
37.9
182.8
186.7
157.3
157.2
67.2
68.5
63.5
59.0
53.9
50.2
42.9
45.9
9.1
8.8
23.1
14.2
2.0
2.8
2.2
1.6
2017 Feb.
3,654.4
3,294.2
360.2 2,129.5 1,760.8
790.4
740.9
627.0
547.5
12-month cumulated transactions as a percentage of GDP
107.5
244.9
31.4
30.5
2017 Feb.
34.0
30.7
1.0
2.3
0.3
0.3
12-month cumulated transactions
3.4
19.8
16.4
7.4
6.9
5.8
5.1
1) The capital account is not seasonally adjusted.
3.9 Euro area external trade in goods 1) , values and volumes by product group 2)
(seasonally adjusted, unless otherwise indicated)
Total (n.s.a.)
Exports (f.o.b.)
Imports (c.i.f.)
Total
Exports Imports
Intermediate
goods
Memo item:
Capital
goods
Consumption
goods
Total
Manufacturing
Intermediate
goods
Memo items:
Capital
goods
Consumption
goods
Manufacturing
Oil
10
11
12
13
1
2
3
4
5
6
7
8
9
Values (EUR billions; annual percentage changes for columns 1 and 2)
2016 Q1
Q2
Q3
Q4
-0.9
0.0
-0.1
2.2
-2.5
-3.6
-1.9
2.2
503.3
505.1
508.9
524.6
233.7
231.6
237.1
244.1
104.9
106.2
103.3
108.3
151.2
153.6
154.4
157.3
422.8
425.5
426.6
439.1
438.1
432.9
442.5
459.7
240.2
237.2
243.6
255.7
72.3
72.1
72.0
74.1
117.1
115.8
117.0
118.8
327.4
321.9
327.2
334.1
37.2
41.8
43.8
50.2
2016 Sep.
Oct.
Nov.
Dec.
2.3
-4.5
5.5
6.0
-1.4
-2.9
5.2
4.6
170.2
170.2
175.6
178.8
79.9
79.0
82.3
82.9
34.5
34.7
35.1
38.6
51.5
51.4
53.2
52.7
142.7
141.6
146.9
150.6
147.2
150.4
153.5
155.8
81.3
82.8
86.0
86.9
23.7
24.9
24.2
25.0
38.9
39.4
39.7
39.7
108.5
110.4
111.8
111.9
14.2
16.1
16.4
17.7
2017 Jan.
Feb.
12.8
4.4
17.0
5.3
177.2
177.9
84.8
.
34.8
.
53.1
.
146.2
148.9
161.5
158.7
92.8
.
25.9
.
39.4
.
113.8
112.0
20.8
.
2016 Q1
Q2
Q3
Q4
-0.7
2.4
0.7
1.3
2.9
5.0
1.6
0.6
117.8
118.1
118.1
120.2
115.9
114.3
116.0
117.9
116.2
117.8
113.5
117.9
121.6
124.0
124.1
124.8
116.8
117.9
117.3
119.7
110.0
108.1
109.0
109.5
110.5
107.0
107.9
108.5
106.7
106.2
106.0
106.1
110.5
111.4
111.5
111.3
111.5
111.1
112.0
112.0
110.9
99.7
100.6
105.2
2016 Aug.
Sep.
Oct.
Nov.
Dec.
9.5
2.8
-4.9
4.7
4.6
7.7
1.0
-2.3
4.6
-0.3
119.5
118.6
117.9
120.8
121.9
116.8
117.3
115.1
119.6
118.9
115.3
113.7
113.8
115.2
124.8
126.1
124.4
124.1
125.9
124.3
118.9
117.9
116.7
120.1
122.3
109.5
108.5
109.2
110.8
108.6
108.3
107.7
107.7
110.8
107.1
109.0
103.9
108.2
105.2
104.8
111.7
111.1
111.9
111.7
110.4
113.2
111.3
112.4
113.0
110.7
99.0
97.4
103.7
108.2
103.7
2017 Jan.
8.9
6.2
119.6
119.8
113.3
123.8
117.8
109.8
110.9
108.2
107.1
111.2
112.8
Volume indices (2000 = 100; annual percentage changes for columns 1 and 2)
Sources: ECB and Eurostat.
1) Differences between ECB’s b.o.p. goods (Table 3.8) and Eurostat’s trade in goods (Table 3.9) are mainly due to different definitions.
2) Product groups as classified in the Broad Economic Categories.
ECB Economic Bulletin, Issue 3 / 2017 - Statistics
S 13
4 Prices and costs
4.1 Harmonised Index of Consumer Prices 1)
(annual percentage changes, unless otherwise indicated)
Total
Index:
2015
= 100
1
Total
Total (s.a.; percentage change vis-à-vis previous period) 2)
Goods Services
Total Processed
food
Total
excluding
food and
energy
Unpro- Non-energy
cessed
industrial
food
goods
Energy
(n.s.a.)
Memo item:
Administered prices
Services
Total HICP Adminisexcluding
tered
administered
prices
prices
2
3
4
5
6
7
8
9
10
11
12
13
% of total
in 2017
100.0 100.0
70.9
55.4
44.6
100.0
12.1
7.5
26.3
9.5
44.6
86.8
13.2
2014
2015
2016
100.0
100.0
100.2
0.4
0.0
0.2
0.8
0.8
0.9
-0.2
-0.8
-0.4
1.2
1.2
1.1
-
-
-
-
-
-
0.2
-0.1
0.2
1.9
0.9
0.2
2016 Q2
Q3
Q4
100.4
100.3
101.0
-0.1
0.3
0.7
0.8
0.8
0.8
-0.9
-0.4
0.4
1.0
1.1
1.1
0.4
0.3
0.4
0.2
0.1
0.3
0.8
1.1
0.0
0.1
0.0
0.1
2.0
0.3
2.4
0.3
0.4
0.2
-0.1
0.3
0.8
0.0
0.3
0.3
2017 Q1
101.0
1.8
0.8
2.3
1.1
0.6
0.3
1.9
0.1
3.3
0.3
2.0
0.5
2016 Oct.
Nov.
Dec.
100.9
100.8
101.3
0.5
0.6
1.1
0.8
0.8
0.9
0.1
0.2
1.0
1.1
1.1
1.3
0.2
0.0
0.4
0.1
0.2
0.1
0.0
0.1
0.7
0.0
0.0
0.0
1.6
-0.2
1.8
0.1
0.0
0.2
0.6
0.6
1.3
0.2
0.3
0.3
2017 Jan.
Feb.
Mar.
100.5
100.8
101.7
1.8
2.0
1.5
0.9
0.9
0.7
2.2
2.6
2.0
1.2
1.3
1.0
0.3
0.2
-0.1
0.1
0.1
0.1
0.8
1.7
-1.6
0.1
-0.1
0.0
2.5
-0.2
-0.8
0.0
0.2
0.1
2.0
2.2
1.7
0.4
0.5
0.7
Goods
Services
Food (including alcoholic
beverages and tobacco)
Total Processed
food
Industrial goods
Unprocessed
food
Total
Non-energy
industrial
goods
Housing
Energy
Transport Communi- Recreation Miscelcation
and laneous
personal
Rents
14
15
16
17
18
19
20
21
22
23
24
25
19.6
12.1
7.5
35.8
26.3
9.5
10.7
6.5
7.3
3.2
15.1
8.2
2014
2015
2016
0.5
1.0
0.9
1.2
0.6
0.6
-0.8
1.6
1.4
-0.5
-1.8
-1.1
0.1
0.3
0.4
-1.9
-6.8
-5.1
1.7
1.2
1.1
1.4
1.1
1.1
1.7
1.3
0.8
-2.8
-0.8
0.0
1.5
1.5
1.4
1.3
1.2
1.2
2016 Q2
Q3
Q4
0.9
1.1
0.8
0.5
0.5
0.6
1.4
2.1
1.0
-1.9
-1.3
0.2
0.5
0.3
0.3
-7.7
-5.1
0.2
1.1
1.1
1.2
1.0
1.0
1.2
0.6
0.9
1.2
0.0
0.0
-0.1
1.3
1.5
1.3
1.2
1.3
1.2
2017 Q1
2.0
0.9
4.0
2.4
0.3
8.2
1.3
1.2
1.7
-1.1
1.4
0.7
2016 Oct.
Nov.
Dec.
0.4
0.7
1.2
0.5
0.7
0.7
0.2
0.7
2.1
-0.1
-0.1
0.9
0.3
0.3
0.3
-0.9
-1.1
2.6
1.1
1.2
1.2
1.2
1.2
1.3
1.0
1.1
1.4
0.0
-0.1
-0.3
1.2
1.1
1.6
1.1
1.2
1.2
2017 Jan.
Feb.
Mar.
1.8
2.5
1.8
0.7
0.8
1.0
3.5
5.3
3.1
2.5
2.6
2.1
0.5
0.2
0.3
8.1
9.3
7.4
1.3
1.2
1.3
1.3
1.2
1.2
1.3
1.9
1.9
-1.0
-0.9
-1.2
1.7
1.7
0.9
0.7
0.8
0.8
% of total
in 2017
Sources: Eurostat and ECB calculations.
1) Data refer to the changing composition of the euro area.
2) In May 2016 the ECB started publishing enhanced seasonally adjusted HICP series for the euro area, following a review of the seasonal adjustment approach as described
in Box 1, Economic Bulletin, Issue 3, ECB, 2016 (https://www.ecb.europa.eu/pub/pdf/ecbu/eb201603.en.pdf).
ECB Economic Bulletin, Issue 3 / 2017 - Statistics
S 14
4 Prices and costs
4.2 Industry, construction and property prices
(annual percentage changes, unless otherwise indicated)
Industrial producer prices excluding construction 1)
Total
(index:
2010 = 100)
Total
Industry excluding construction and energy
Manufacturing
Total Intermediate
goods
Capital
goods
Residential
property
prices 2)
Experimental
indicator of
commercial
property
prices 2)
11
12
13
Consumer goods
Total
1
Construction
Energy
Food, Nonbeverages food
and tobacco
2
3
4
5
6
7
8
9
10
% of total
in 2010
100.0 100.0
78.1
72.1
29.4
20.1
22.6
13.8
8.9
27.9
2014
2015
2016
106.9
104.0
101.6
-1.5
-2.7
-2.3
-0.9
-2.4
-1.5
-0.3
-0.5
-0.5
-1.1
-1.3
-1.7
0.4
0.7
0.4
0.1
-0.6
0.0
-0.2
-1.0
-0.1
0.3
0.2
0.1
-4.3
-8.2
-6.9
0.3
0.2
0.4
0.4
1.6
3.3
1.4
4.5
5.4
2016 Q1
Q2
Q3
Q4
100.6
100.9
101.9
103.1
-3.7
-3.8
-2.0
0.4
-2.7
-2.8
-1.3
1.0
-0.9
-1.1
-0.6
0.4
-2.2
-2.7
-1.8
0.0
0.4
0.4
0.4
0.5
-0.4
-0.5
0.0
0.8
-0.5
-0.8
-0.1
1.2
0.0
0.1
0.1
0.1
-11.1
-10.7
-5.9
0.4
-0.2
0.2
0.4
1.1
2.8
3.1
3.4
3.8
5.4
3.1
8.8
4.3
2016 Sep.
Oct.
Nov.
Dec.
101.9
102.6
102.9
103.7
-1.5
-0.5
0.0
1.6
-0.7
0.3
0.5
2.3
-0.3
0.0
0.4
0.9
-1.4
-0.8
0.1
0.8
0.4
0.5
0.5
0.6
0.1
0.6
0.7
1.1
0.2
0.6
1.1
1.7
0.1
0.2
0.1
0.0
-4.5
-1.6
-0.8
3.8
-
-
-
2017 Jan.
Feb.
104.8
104.8
3.9
4.5
3.7
4.4
1.5
2.1
2.2
3.3
0.7
0.8
1.5
1.7
2.1
2.5
0.2
0.2
10.5
11.4
-
-
-
Sources: Eurostat, ECB calculations, and ECB calculations based on MSCI data and national sources (col. 13).
1) Domestic sales only.
2) Experimental data based on non-harmonised sources (see https://www.ecb.europa.eu/stats/ecb_statistics/governance_and_quality_framework/html/experimental-data.en.html
for further details).
4.3 Commodity prices and GDP deflators
(annual percentage changes, unless otherwise indicated)
GDP deflators
Total Total
Domestic demand
Exports
(s.a.;
index:
Total
Private GovernGross
2010
consumpment
fixed
= 100)
tion consumpcapital
tion formation
1
2
3
4
5
6
1)
Imports
1)
Oil prices
(EUR per
barrel)
Non-energy commodity prices (EUR)
Import-weighted 2)
Use-weighted 2)
Total Food Non-food
7
8
9
% of total
10
11
13
14
15
100.0
45.4
54.6 100.0
50.4
49.6
-8.5
-4.5
-3.2
-0.4
4.6
2.9
7.0
-7.3 -10.3
-6.4
-2.7
-2.9
-12.5 -12.5 -12.6
1.4 -5.8 -10.6
18.6
3.3
-6.7
-12.3
1.3
18.5
2014
2015
2016
104.6
105.8
106.7
0.9
1.1
0.9
0.6
0.3
0.5
0.5
0.1
0.4
0.9
0.5
0.8
0.6
0.7
0.8
-0.7
0.1
-1.3
-1.5
-1.9
-2.4
74.1
47.1
39.9
-3.4
0.0
-3.5
2.0
4.2
-3.9
2016 Q2
Q3
Q4
106.5
106.7
107.2
0.9
0.9
0.8
0.2
0.6
0.9
0.1
0.3
0.7
0.7
0.8
0.8
0.7
0.8
1.2
-2.4
-1.5
0.0
-4.1
-2.2
0.1
40.8
41.0
46.5
-9.0
-0.5
9.1
-5.7
-2.1
1.1
12
Total Food Non-food
2017 Q1
.
.
.
.
.
.
.
.
50.8
18.3
5.9
33.2
13.0
0.1
32.4
2016 Oct.
Nov.
Dec.
-
-
-
-
-
-
-
-
45.1
43.1
51.3
3.1
8.5
15.7
-0.3
-0.1
3.9
7.1
19.0
30.2
-2.9 -10.3
2.4
-8.1
10.6
-1.4
8.3
18.7
28.8
2017 Jan.
Feb.
Mar.
-
-
-
-
-
-
-
-
51.6
52.2
48.7
19.2
21.4
14.6
7.2
8.0
2.7
34.0
37.4
28.5
13.1
15.5
10.5
32.0
36.0
29.3
0.9
1.7
-2.2
Sources: Eurostat, ECB calculations and Bloomberg (col. 9).
1) Deflators for exports and imports refer to goods and services and include cross-border trade within the euro area.
2) Import-weighted: weighted according to 2009-11 average import structure; use-weighted: weighted according to 2009-11 average domestic demand structure.
ECB Economic Bulletin, Issue 3 / 2017 - Statistics
S 15
4 Prices and costs
4.4 Price-related opinion surveys
(seasonally adjusted)
European Commission Business and Consumer Surveys
(percentage balances)
Selling price expectations
(for next three months)
Manufacturing
Retail trade
1
Services
Construction
Purchasing Managers’ Surveys
(diffusion indices)
Consumer
price trends
over past
12 months
Input prices
Manufacturing
Prices charged
Services
Manufacturing
Services
2
3
4
5
6
7
4.7
-
-
-2.0
34.0
57.7
56.7
-
49.9
2014
2015
2016
-0.9
-2.8
-0.4
-1.5
1.3
1.7
0.9
2.6
4.4
-17.4
-13.2
-7.3
14.2
-1.2
-0.7
49.6
48.9
49.8
53.5
53.5
53.9
49.7
49.6
49.3
48.2
49.0
49.6
2016 Q2
Q3
Q4
-1.0
-0.2
4.6
1.9
1.0
3.1
4.6
4.5
4.9
-8.1
-6.6
-5.4
-2.2
-0.3
1.6
47.5
51.4
58.6
54.4
54.0
54.9
48.5
49.6
51.6
49.0
49.8
50.5
2017 Q1
9.0
5.5
6.4
-3.7
12.0
67.8
56.7
55.0
51.4
2016 Nov.
Dec.
4.9
5.4
2.8
4.0
5.3
4.9
-6.0
-5.1
1.8
2.8
58.8
63.2
54.4
56.0
51.4
52.5
50.3
51.4
2017 Jan.
Feb.
Mar.
Apr.
8.3
9.0
9.8
.
4.9
6.3
5.2
.
6.7
6.4
6.1
.
-5.1
-3.1
-2.9
.
8.3
12.9
14.9
.
67.0
68.3
68.1
67.4
56.4
56.9
56.8
57.2
54.0
55.4
55.6
55.6
50.9
51.1
52.2
52.0
Mainly non-business
economy
Memo item:
Indicator of
negotiated
wages 1)
7
1999-13
8
9
Sources: European Commission (Directorate-General for Economic and Financial Affairs) and Markit.
4.5 Labour cost indices
(annual percentage changes, unless otherwise indicated)
Total
(index:
2012 = 100)
Total
By component
Wages and
salaries
For selected economic activities
Employers’ social
contributions
Business economy
1
2
3
4
5
6
% of total
in 2012
100.0
100.0
74.6
25.4
69.3
30.7
2014
2015
2016
102.6
104.2
105.7
1.2
1.5
1.4
1.3
1.9
1.4
1.2
0.5
1.4
1.2
1.5
1.3
1.2
1.6
1.6
1.7
1.5
1.4
2016 Q1
Q2
Q3
Q4
98.9
109.1
102.5
112.2
1.6
1.1
1.4
1.6
1.6
0.9
1.6
1.6
1.4
1.4
1.1
1.5
1.5
0.9
1.2
1.7
1.7
1.3
1.7
1.5
1.4
1.5
1.5
1.4
Sources: Eurostat and ECB calculations.
1) Experimental data based on non-harmonised sources (see https://www.ecb.europa.eu/stats/ecb_statistics/governance_and_quality_framework/html/experimental-data.en.html
for further details).
ECB Economic Bulletin, Issue 3 / 2017 - Statistics
S 16
4 Prices and costs
4.6 Unit labour costs, compensation per labour input and labour productivity
(annual percentage changes, unless otherwise indicated; quarterly data seasonally adjusted; annual data unadjusted)
Total Total
(index:
2010
=100)
By economic activity
Agriculture,
ManuConTrade, Information Finance
Real Professional,
forestry facturing, struction
transport, and commuand estate business and
and fishing energy and
accomnication insurance
support
utilities
modation and
services
food services
1
2
3
4
5
6
Unit labour costs
2014
2015
2016
104.6
104.8
105.7
0.7
0.3
0.8
-1.0
1.0
2.3
-0.7
-2.3
0.1
1.1
1.0
-0.4
2016 Q1
Q2
Q3
Q4
105.5
105.6
105.9
106.2
1.0
0.9
0.8
0.9
1.1
2.0
2.4
4.0
0.3
0.2
0.5
-0.3
-0.1
-0.5
-0.8
-0.2
2014
2015
2016
106.5
107.9
109.3
1.3
1.3
1.3
0.2
1.4
0.7
2.0
1.7
1.1
1.8
0.8
1.4
1.1
1.5
1.3
2016 Q1
Q2
Q3
Q4
108.9
109.1
109.5
110.1
1.3
1.2
1.3
1.5
0.7
1.0
0.2
1.1
1.3
0.8
1.2
1.2
1.3
1.3
1.6
1.6
1.3
1.3
1.2
1.5
2014
2015
2016
101.9
102.9
103.4
0.6
1.0
0.5
1.2
0.4
-1.5
2.8
4.1
1.0
0.6
-0.2
1.8
0.5
0.9
0.7
2016 Q1
Q2
Q3
Q4
103.3
103.2
103.5
103.7
0.3
0.2
0.5
0.6
-0.4
-1.0
-2.1
-2.8
1.0
0.6
0.7
1.6
1.4
1.8
2.3
1.8
0.6
0.3
0.6
0.9
2014
2015
2016
108.5
109.7
111.3
1.2
1.1
1.5
1.2
1.3
0.2
1.5
1.2
1.0
1.4
0.1
1.8
1.2
1.4
1.4
2016 Q1
Q2
Q3
Q4
110.5
110.7
111.3
112.1
1.2
1.2
1.6
1.8
-1.0
0.1
0.7
1.3
0.9
0.4
1.2
1.0
1.2
1.3
2.2
2.3
1.2
1.3
1.1
1.9
2014
2015
2016
104.1
105.0
105.7
0.7
0.9
0.6
1.8
-0.6
-1.8
2.4
3.7
0.9
0.3
-0.9
2.0
0.8
1.1
0.8
2016 Q1
Q2
Q3
Q4
105.3
105.1
105.6
106.0
0.3
0.2
0.8
0.9
-1.8
-2.0
-1.9
-2.3
0.7
0.3
0.8
1.4
1.0
1.6
2.6
2.8
0.5
0.2
0.6
1.3
Public administration,
education,
health and
social work
Arts, entertainment
and other
services
7
8
9
10
11
12
0.6
0.6
0.6
-0.6
0.7
0.1
2.1
0.6
1.1
1.8
3.5
4.0
1.2
1.8
1.2
1.6
1.1
1.6
1.4
1.9
1.7
0.7
1.0
0.6
0.5
0.9
0.3
-0.5
-0.2
1.2
1.4
1.1
0.7
4.4
3.7
3.7
4.2
2.1
0.9
0.7
1.5
1.6
1.6
1.6
1.6
2.7
1.5
1.4
1.4
2.2
2.4
1.2
1.7
0.6
1.4
1.5
2.5
3.4
1.6
1.6
1.4
1.1
1.1
1.5
1.1
1.0
2.1
1.3
1.2
1.0
1.3
1.7
1.0
1.6
1.2
3.6
3.5
2.8
3.5
1.5
1.3
1.0
1.6
1.3
1.3
1.6
1.7
2.1
1.8
2.2
2.5
2.8
1.7
1.1
-0.4
0.0
0.3
-0.3
-0.9
-0.6
0.4
-0.2
0.1
-0.5
0.0
-0.1
-0.4
-0.9
0.4
0.3
0.9
1.6
1.5
0.5
-0.4
0.5
0.6
-0.8
-0.2
-0.8
-0.7
-0.6
0.4
0.4
0.1
-0.3
-0.3
0.0
0.1
-0.6
0.2
0.9
1.1
2.1
1.2
1.3
1.7
0.7
1.3
1.3
2.1
3.6
1.2
1.3
1.3
0.9
1.0
1.8
1.2
1.0
2.7
1.0
0.9
1.3
1.8
1.4
0.7
1.7
1.4
3.0
3.2
3.6
4.8
1.0
1.1
1.2
1.7
1.6
1.9
2.1
1.7
2.6
2.5
3.3
3.2
2.8
0.6
1.3
-0.3
-0.1
0.1
-0.1
-1.4
-0.5
0.3
-0.3
0.1
-0.6
-0.1
0.2
-0.1
-0.9
0.7
0.0
0.7
2.0
2.1
0.3
-1.1
0.5
0.7
-0.7
-0.9
-0.3
-0.1
-1.0
-0.1
0.8
0.5
0.0
0.2
0.5
0.2
-0.3
0.4
1.5
1.5
Compensation per employee
Labour productivity per person employed
Compensation per hour worked
Hourly labour productivity
Sources: Eurostat and ECB calculations.
ECB Economic Bulletin, Issue 3 / 2017 - Statistics
S 17
5 Money and credit
5.1 Monetary aggregates 1)
(EUR billions and annual growth rates; seasonally adjusted; outstanding amounts and growth rates at end of period; transactions during period)
M3
M2
M3-M2
M1
M2-M1
Currency Overnight
in
deposits
circulation
Deposits
Deposits
with an redeemable
agreed
at notice
maturity
of up to
of up to
3 months
2 years
5
6
Outstanding amounts
Repos
Money
market
fund
shares
Debt
securities
with
a maturity
of up to
2 years
7
8
9
10
11
12
1
2
3
4
2014
2015
2016
969.5
1,036.5
1,073.1
4,970.5
5,566.3
6,117.1
5,939.9
6,602.8
7,190.2
1,581.7
1,439.2
1,320.9
2,149.8
2,161.8
2,175.8
3,731.5
3,601.0
3,496.7
9,671.4
10,203.8
10,686.9
121.5
74.6
70.4
422.2
479.0
521.5
107.0
73.6
96.4
650.7
627.2
688.4
10,322.1
10,831.1
11,375.2
2016 Q1
Q2
Q3
Q4
1,049.6
1,054.6
1,066.6
1,073.1
5,711.9
5,821.2
5,946.7
6,117.1
6,761.6
6,875.8
7,013.3
7,190.2
1,421.0
1,411.0
1,393.3
1,320.9
2,164.8
2,171.9
2,174.5
2,175.8
3,585.8
3,582.9
3,567.8
3,496.7
10,347.3
10,458.7
10,581.1
10,686.9
85.3
84.2
80.5
70.4
465.5
481.7
496.0
521.5
94.9
94.8
93.8
96.4
645.8
660.7
670.2
688.4
10,993.1
11,119.4
11,251.3
11,375.2
2016 Sep.
Oct.
Nov.
Dec.
1,066.6
1,072.4
1,075.2
1,073.1
5,946.7
5,981.7
6,069.9
6,117.1
7,013.3
7,054.1
7,145.1
7,190.2
1,393.3
1,361.2
1,350.4
1,320.9
2,174.5
2,175.0
2,171.9
2,175.8
3,567.8
3,536.2
3,522.4
3,496.7
10,581.1
10,590.3
10,667.5
10,686.9
80.5
74.4
72.5
70.4
496.0
503.7
506.1
521.5
93.8
91.4
98.7
96.4
670.2
669.5
677.3
688.4
11,251.3
11,259.8
11,344.7
11,375.2
2017 Jan.
Feb. (p)
1,081.8
1,086.2
6,154.9
6,208.6
7,236.7
7,294.8
1,329.9
1,326.1
2,178.1
2,178.0
3,508.0
3,504.1
10,744.7
10,798.9
75.1
66.8
515.5
507.1
98.2
98.7
688.9
672.6
11,433.6
11,471.5
2014
2015
2016
59.0
65.9
36.7
374.9
562.6
544.6
433.9
628.5
581.3
-91.8
-135.4
-107.9
3.7
12.2
16.0
-88.1
-123.2
-91.9
345.8
505.3
489.4
3.6
-48.0
-4.3
10.4
51.4
42.3
13.3
-26.3
17.6
27.3
-22.9
55.7
373.1
482.5
545.1
2016 Q1
Q2
Q3
Q4
13.3
5.0
12.0
6.5
156.1
104.4
127.9
156.2
169.4
109.3
139.9
162.6
-14.0
-12.7
-15.7
-65.5
3.1
7.2
2.3
3.4
-10.9
-5.5
-13.4
-62.1
158.6
103.8
126.5
100.5
11.2
-1.4
-3.7
-10.4
-13.4
15.5
14.7
25.5
19.2
-1.4
-2.4
2.1
17.0
12.7
8.6
17.3
175.6
116.6
135.2
117.8
2016 Sep.
Oct.
Nov.
Dec.
5.0
5.9
2.8
-2.1
25.1
28.4
81.3
46.5
30.2
34.2
84.0
44.4
0.6
-25.0
-12.8
-27.7
0.3
0.7
-1.2
3.9
0.9
-24.3
-14.0
-23.8
31.0
9.9
70.0
20.6
-1.8
-6.2
-2.1
-2.1
15.0
7.7
2.4
15.4
-5.1
-3.8
8.1
-2.1
8.1
-2.3
8.4
11.2
39.1
7.7
78.3
31.8
2017 Jan.
Feb. (p)
8.7
4.3
41.5
50.3
50.3
54.6
11.6
-4.4
2.2
-0.1
13.8
-4.6
64.1
50.0
4.8
-8.4
-6.0
-8.4
1.1
0.3
-0.1
-16.6
64.0
33.5
2014
2015
2016
6.5
6.8
3.5
8.4
11.3
9.8
8.0
10.5
8.8
-5.4
-8.6
-7.5
0.2
0.6
0.7
-2.3
-3.3
-2.6
3.7
5.2
4.8
2.9
-39.1
-5.8
2.5
12.0
8.8
19.9
-25.3
23.7
4.4
-3.5
8.8
3.8
4.7
5.0
2016 Q1
Q2
Q3
Q4
6.0
4.0
3.7
3.5
11.1
9.7
9.3
9.8
10.3
8.8
8.4
8.8
-6.2
-4.1
-3.3
-7.5
0.6
0.6
0.5
0.7
-2.2
-1.3
-1.0
-2.6
5.6
5.1
5.0
4.8
-25.9
1.1
-12.8
-5.8
6.6
9.2
8.4
8.8
-1.1
-3.0
13.6
23.7
-0.4
6.1
5.9
8.8
5.2
5.1
5.1
5.0
2016 Sep.
Oct.
Nov.
Dec.
3.7
4.0
3.8
3.5
9.3
8.8
9.4
9.8
8.4
8.0
8.5
8.8
-3.3
-4.7
-5.5
-7.5
0.5
0.6
0.6
0.7
-1.0
-1.5
-1.9
-2.6
5.0
4.6
4.9
4.8
-12.8
-27.3
-15.8
-5.8
8.4
6.8
4.9
8.8
13.6
13.6
12.1
23.7
5.9
2.2
3.1
8.8
5.1
4.5
4.8
5.0
2017 Jan.
Feb. (p)
3.6
3.9
9.3
9.2
8.4
8.4
-6.7
-6.2
0.8
0.7
-2.2
-2.1
4.7
4.8
-7.3
-24.3
8.9
8.1
12.0
6.8
7.3
3.5
4.8
4.7
Transactions
Growth rates
Source: ECB.
1) Data refer to the changing composition of the euro area.
ECB Economic Bulletin, Issue 3 / 2017 - Statistics
S 18
5 Money and credit
5.2 Deposits in M3 1)
(EUR billions and annual growth rates; seasonally adjusted; outstanding amounts and growth rates at end of period; transactions during period)
Non-financial corporations 2)
Total Overnight
Households 3)
Financial Insurance
Other
corporcorpor- general
ations
ations governother than
and ment 4)
MFIs and
pension
ICPFs 2)
funds
With an
agreed
maturity
of up to
2 years
Redeemable
at notice
of up to
3 months
Repos
Total Overnight
With an
agreed
maturity
of up to
2 years
Redeemable
at notice
of up to
3 months
Repos
6
7
Outstanding amounts
8
9
10
11
12
13
1
2
3
4
5
2014
2015
2016
1,845.1
1,930.5
2,056.1
1,349.1
1,483.9
1,636.6
365.1
321.7
293.9
111.6
116.4
117.0
19.4
8.4
8.6
5,557.7
5,750.9
6,049.7
2,749.5
3,059.7
3,399.7
812.1
695.1
643.6
1,993.2
1,993.7
2,004.8
2.8
2.4
1.7
865.5
970.1
1,001.3
222.2
225.8
196.5
332.9
364.7
380.6
2016 Q1
Q2
Q3
Q4
1,984.8
2,013.7
2,047.5
2,056.1
1,536.6
1,574.3
1,602.5
1,636.6
322.7
314.0
317.8
293.9
116.0
117.1
118.1
117.0
9.4
8.4
9.1
8.6
5,829.7
5,906.0
5,979.5
6,049.7
3,137.1
3,214.2
3,301.8
3,399.7
693.6
688.8
672.0
643.6
1,996.3
2,000.0
2,003.1
2,004.8
2.7
3.0
2.6
1.7
973.7
978.0
975.5
1,001.3
218.9
210.7
206.2
196.5
375.9
379.9
386.3
380.6
2016 Sep.
Oct.
Nov.
Dec.
2,047.5
2,037.3
2,064.6
2,056.1
1,602.5
1,604.6
1,634.0
1,636.6
317.8
307.6
305.1
293.9
118.1
118.1
117.1
117.0
9.1
7.0
8.5
8.6
5,979.5
6,001.8
6,029.6
6,049.7
3,301.8
3,334.4
3,372.2
3,399.7
672.0
660.0
652.0
643.6
2,003.1
2,004.6
2,002.9
2,004.8
2.6
2.8
2.5
1.7
975.5
953.4
981.1
1,001.3
206.2
206.5
206.3
196.5
386.3
393.2
383.1
380.6
2,099.5
2017 Jan.
Feb. (p) 2,120.5
1,677.2
1,695.9
299.3
301.8
116.0
116.0
7.0
6.8
6,087.9
6,112.3
3,438.6
3,469.4
636.1
628.0
2,010.5
2,012.0
2.7
2.8
963.1
959.5
194.6
195.4
392.9
391.9
68.7
81.8
128.9
91.1
121.7
152.8
-26.7
-33.5
-24.1
1.5
4.9
0.0
2.8
-11.2
0.2
140.7
193.4
301.4
208.8
303.0
335.5
-65.0
-109.9
-46.8
-1.4
0.8
13.4
-1.7
-0.4
-0.8
52.7
86.1
30.4
7.3
-0.1
-29.3
21.0
30.3
17.1
2016 Q1
Q2
Q3
Q4
61.2
27.3
34.8
5.6
57.8
36.3
29.5
29.2
2.7
-8.9
4.0
-21.9
-0.4
1.0
0.6
-1.3
1.1
-1.1
0.7
-0.5
80.9
75.5
73.7
71.3
78.5
76.2
87.7
93.1
-0.6
-5.1
-16.6
-24.4
2.8
4.0
3.1
3.5
0.3
0.4
-0.5
-0.9
8.8
-0.5
0.4
21.7
-6.5
-8.5
-4.2
-10.0
12.1
3.7
6.2
-4.9
2016 Sep.
Oct.
Nov.
Dec.
15.7
-9.3
23.8
-8.9
6.8
0.6
26.4
2.2
7.8
-7.8
-3.0
-11.1
0.7
-0.1
-1.1
-0.1
0.4
-2.1
1.5
0.1
18.7
23.4
28.1
19.8
24.6
29.1
36.8
27.3
-5.5
-7.3
-8.6
-8.5
-0.1
1.4
0.2
1.9
-0.2
0.2
-0.3
-0.8
-3.4
-23.9
23.7
21.9
-7.0
0.2
-0.4
-9.8
0.2
7.5
-10.1
-2.4
2017 Jan.
Feb. (p)
46.0
19.5
42.4
17.2
6.2
2.5
-1.0
0.0
-1.6
-0.2
38.9
23.3
39.5
30.3
-7.3
-8.7
5.6
1.5
1.0
0.1
-35.3
-5.7
-1.8
0.7
12.3
-0.5
2014
2015
2016
4.0
4.4
6.7
7.6
9.0
10.3
-6.7
-9.4
-7.6
1.3
4.4
0.0
15.9
-57.4
2.2
2.6
3.5
5.2
8.2
11.0
11.0
-7.4
-13.6
-6.8
-0.1
0.0
0.7
-37.8
-15.1
-31.2
6.5
9.8
3.1
3.9
0.0
-13.0
7.0
9.1
4.7
2016 Q1
Q2
Q3
Q4
7.4
8.0
7.4
6.7
11.0
11.1
9.9
10.3
-4.5
-2.9
-1.3
-7.6
3.8
3.9
1.7
0.0
-31.3
-27.8
-8.5
2.2
4.2
4.6
5.1
5.2
10.7
10.4
10.6
11.0
-8.8
-5.9
-4.9
-6.8
0.2
0.1
0.4
0.7
-30.6
0.3
-18.2
-31.2
6.2
4.2
1.1
3.1
-3.3
-8.5
-5.7
-13.0
10.3
10.3
7.7
4.7
2016 Sep.
Oct.
Nov.
Dec.
7.4
5.5
7.1
6.7
9.9
7.9
10.1
10.3
-1.3
-2.8
-3.7
-7.6
1.7
0.9
-0.1
0.0
-8.5
-29.6
-5.3
2.2
5.1
5.2
5.4
5.2
10.6
10.7
11.1
11.0
-4.9
-5.5
-6.0
-6.8
0.4
0.6
0.7
0.7
-18.2
-19.8
-32.6
-31.2
1.1
-1.0
0.5
3.1
-5.7
-9.4
-8.0
-13.0
7.7
7.8
3.1
4.7
2017 Jan.
Feb. (p)
7.1
7.6
10.5
10.9
-5.4
-4.6
-0.2
-0.5
-26.8
-26.6
5.5
5.4
11.4
11.5
-7.8
-8.9
0.9
0.9
-19.6
-4.4
-1.0
-2.0
-13.5
-15.4
5.6
5.3
Transactions
2014
2015
2016
Growth rates
Source: ECB.
1) Data refer to the changing composition of the euro area.
2) In accordance with the ESA 2010, in December 2014 holding companies of non-financial groups were reclassified from the non-financial corporations sector to the financial
corporations sector. These entities are included in MFI balance sheet statistics with financial corporations other than MFIs and insurance corporations and pension funds (ICPFs).
3) Including non-profit institutions serving households.
4) Refers to the general government sector excluding central government.
ECB Economic Bulletin, Issue 3 / 2017 - Statistics
S 19
5 Money and credit
5.3 Credit to euro area residents 1)
(EUR billions and annual growth rates; seasonally adjusted; outstanding amounts and growth rates at end of period; transactions during period)
Credit to general government
Total
Loans
Credit to other euro area residents
Debt
securities
Total
Loans
Total
4
Debt
securities
Equity and
non-money
market fund
investment
fund shares
To non- To house- To financial To insurance
financial
holds 4) corporations corporations
Adjusted corporother than and pension
loans 2) ations 3)
MFIs and
funds
ICPFs 3)
1
2
3
5
6
7
Outstanding amounts
8
9
10
11
12
2014
2015
2016
3,615.6
3,904.2
4,397.5
1,135.0
1,112.3
1,082.0
2,478.5
2,789.5
3,302.3
12,504.8 10,454.5
12,599.4 10,512.0
12,844.9 10,673.5
10,726.7
10,807.4
10,981.2
4,299.6
4,274.5
4,300.9
5,200.7
5,307.6
5,409.3
825.1
806.3
850.8
129.0
123.5
112.5
1,280.0
1,305.1
1,385.2
770.3
782.3
786.2
2016 Q1
Q2
Q3
Q4
4,053.6
4,191.8
4,272.2
4,397.5
1,115.9
1,112.5
1,105.2
1,082.0
2,924.6
3,066.2
3,153.6
3,302.3
12,629.6
12,664.0
12,769.1
12,844.9
10,561.2
10,566.1
10,623.5
10,673.5
10,824.5
10,870.4
10,927.4
10,981.2
4,288.8
4,297.1
4,289.6
4,300.9
5,338.9
5,348.3
5,379.3
5,409.3
824.8
816.8
845.5
850.8
108.8
103.9
109.1
112.5
1,312.2
1,342.5
1,365.2
1,385.2
756.2
755.4
780.5
786.2
2016 Sep.
Oct.
Nov.
Dec.
4,272.2
4,291.1
4,320.9
4,397.5
1,105.2
1,099.6
1,092.5
1,082.0
3,153.6
3,178.1
3,215.0
3,302.3
12,769.1
12,810.3
12,851.3
12,844.9
10,623.5
10,656.5
10,699.4
10,673.5
10,927.4
10,956.9
10,981.8
10,981.2
4,289.6
4,302.9
4,321.0
4,300.9
5,379.3
5,388.3
5,407.2
5,409.3
845.5
850.8
855.3
850.8
109.1
114.5
115.9
112.5
1,365.2
1,373.1
1,379.0
1,385.2
780.5
780.7
772.9
786.2
2017 Jan.
Feb. (p)
4,388.7
4,405.0
1,087.3
1,073.2
3,287.7
3,318.0
12,886.4 10,696.3
12,916.1 10,719.6
10,995.5
11,011.8
4,316.3
4,323.0
5,422.6
5,443.4
842.8
841.6
114.6
111.6
1,404.0
1,403.6
786.1
792.9
2014
2015
2016
73.8
284.9
458.8
16.4
-21.1
-34.9
57.4
305.7
493.6
-102.0
86.7
318.4
-47.1
58.1
232.5
-33.3
73.2
251.0
-61.1
-13.1
81.6
-14.9
98.2
119.3
17.2
-21.4
42.7
11.7
-5.7
-11.1
-89.8
25.1
80.6
35.0
3.5
5.2
2016 Q1
Q2
Q3
Q4
120.0
116.4
69.3
153.2
1.5
-8.9
-7.3
-20.3
118.5
125.2
76.3
173.6
69.3
54.8
113.3
81.0
79.3
22.1
70.3
60.9
52.2
64.6
72.1
62.0
35.9
19.5
6.6
19.7
36.2
14.5
33.8
34.8
21.8
-6.9
24.8
3.1
-14.6
-5.0
5.2
3.3
11.0
31.1
20.9
17.6
-21.0
1.6
22.1
2.6
2016 Sep.
Oct.
Nov.
Dec.
12.2
38.8
45.3
69.0
-2.6
-5.5
-7.0
-7.8
14.8
44.3
52.3
77.0
24.2
44.0
36.3
0.6
20.7
33.7
37.6
-10.4
22.2
29.9
20.6
11.4
-1.3
16.0
16.1
-12.4
14.7
7.2
18.9
8.7
8.7
5.0
1.3
-3.2
-1.4
5.5
1.3
-3.5
1.2
7.7
5.5
4.3
2.3
2.6
-6.8
6.7
2017 Jan.
Feb. (p)
16.2
8.0
5.3
-13.0
10.5
20.9
54.2
23.4
31.0
19.9
24.8
12.5
18.4
5.1
14.5
19.9
-4.0
-2.2
2.1
-3.0
19.5
-1.2
3.7
4.7
2014
2015
2016
2.1
7.9
11.7
1.5
-1.9
-3.1
2.4
12.3
17.6
-0.8
0.7
2.5
-0.4
0.6
2.2
-0.3
0.7
2.3
-1.4
-0.3
1.9
-0.3
1.9
2.3
1.8
-2.6
5.3
11.9
-4.4
-9.0
-6.6
2.0
6.2
4.4
0.4
0.7
2016 Q1
Q2
Q3
Q4
10.2
11.7
10.1
11.7
-2.8
-2.8
-2.5
-3.1
16.1
18.1
15.3
17.6
1.2
1.5
2.0
2.5
1.2
1.2
1.9
2.2
1.1
1.6
2.1
2.3
0.9
1.3
1.5
1.9
2.2
1.9
2.1
2.3
0.1
0.3
4.9
5.3
-19.2
-23.6
-10.7
-9.0
3.1
7.2
3.5
6.2
-2.3
-2.9
0.8
0.7
2016 Sep.
Oct.
Nov.
Dec.
10.1
10.6
10.7
11.7
-2.5
-2.6
-3.0
-3.1
15.3
16.0
16.3
17.6
2.0
2.3
2.4
2.5
1.9
2.0
2.1
2.2
2.1
2.2
2.2
2.3
1.5
1.7
1.8
1.9
2.1
1.9
2.1
2.3
4.9
5.6
4.2
5.3
-10.7
-7.8
-6.7
-9.0
3.5
5.4
7.4
6.2
0.8
0.5
-0.7
0.7
2017 Jan.
Feb. (p)
10.5
9.8
-2.9
-3.9
15.8
15.1
2.7
2.6
2.2
2.0
2.4
2.3
1.8
1.5
2.4
2.4
4.5
3.8
-8.6
-11.4
7.1
6.7
2.6
3.6
Transactions
Growth rates
Source: ECB.
1) Data refer to the changing composition of the euro area.
2) Adjusted for loan sales and securitisation (resulting in derecognition from the MFI statistical balance sheet) as well as for positions arising from notional cash pooling services
provided by MFIs.
3) In accordance with the ESA 2010, in December 2014 holding companies of non-financial groups were reclassified from the non-financial corporations sector to the financial
corporations sector. These entities are included in MFI balance sheet statistics with financial corporations other than MFIs and insurance corporations and pension funds (ICPFs).
4) Including non-profit institutions serving households.
ECB Economic Bulletin, Issue 3 / 2017 - Statistics
S 20
5 Money and credit
5.4 MFI loans to euro area non-financial corporations and households 1)
(EUR billions and annual growth rates; seasonally adjusted; outstanding amounts and growth rates at end of period; transactions during period)
Non-financial corporations 2)
Total
Up to 1 year
Adjusted
loans 4)
Households 3)
Over 1 Over 5 years
and up to
5 years
1
2
3
4
5
Outstanding amounts
2014
2015
2016
4,299.6
4,274.5
4,300.9
4,253.9
4,257.7
4,301.7
1,109.8
1,038.4
997.3
720.7
758.5
796.3
2016 Q1
Q2
Q3
Q4
4,288.8
4,297.1
4,289.6
4,300.9
4,261.6
4,278.6
4,279.7
4,301.7
1,048.5
1,040.4
1,009.4
997.3
2016 Sep.
Oct.
Nov.
Dec.
4,289.6
4,302.9
4,321.0
4,300.9
4,279.7
4,288.6
4,298.0
4,301.7
2017 Jan.
Feb. (p)
4,316.3
4,323.0
Total
Loans for
consumption
Loans for
house
purchase
Other loans
Adjusted
loans 4)
6
7
8
9
10
2,469.1
2,477.6
2,507.3
5,200.7
5,307.6
5,409.3
5,546.1
5,640.6
5,725.9
563.5
595.9
616.5
3,860.9
3,948.4
4,042.5
776.4
763.3
750.3
768.6
774.9
786.9
796.3
2,471.6
2,481.8
2,493.3
2,507.3
5,338.9
5,348.3
5,379.3
5,409.3
5,659.1
5,683.5
5,701.1
5,725.9
602.6
604.1
608.5
616.5
3,974.9
3,986.3
4,018.2
4,042.5
761.4
757.9
752.6
750.3
1,009.4
1,022.9
1,030.8
997.3
786.9
787.3
794.8
796.3
2,493.3
2,492.7
2,495.3
2,507.3
5,379.3
5,388.3
5,407.2
5,409.3
5,701.1
5,712.5
5,723.1
5,725.9
608.5
612.8
614.9
616.5
4,018.2
4,019.3
4,035.8
4,042.5
752.6
756.2
756.5
750.3
4,310.3
4,314.7
1,012.2
1,010.6
798.1
796.8
2,506.0
2,515.6
5,422.6
5,443.4
5,743.3
5,756.7
620.7
623.5
4,052.2
4,072.3
749.8
747.7
-61.1
-13.1
81.6
-68.4
21.1
96.9
-14.2
-64.3
-17.5
2.3
32.4
45.2
-49.2
18.9
54.0
-14.9
98.2
119.3
5.6
76.1
111.2
-3.0
21.9
23.7
-3.2
79.9
105.9
-8.7
-3.6
-10.4
35.9
19.5
6.6
19.7
28.1
28.5
10.8
29.5
19.2
-4.1
-23.1
-9.4
13.2
8.6
14.9
8.5
3.5
15.0
14.8
20.6
36.2
14.5
33.8
34.8
24.7
29.5
27.4
29.6
8.0
1.6
5.1
9.0
28.6
13.5
32.5
31.4
-0.4
-0.6
-3.8
-5.6
-1.3
16.0
16.1
-12.4
1.9
11.3
8.3
9.9
-11.8
13.3
6.7
-29.4
5.8
0.9
6.9
0.7
4.7
1.8
2.6
16.3
14.7
7.2
18.9
8.7
9.9
9.7
10.8
9.1
1.3
4.4
2.2
2.4
14.8
4.5
16.1
10.8
-1.5
-1.7
0.6
-4.5
2017 Jan.
Feb. (p)
18.4
5.1
13.2
2.7
15.9
-1.8
2.0
-1.1
0.6
8.1
14.5
19.9
19.1
12.3
4.6
1.7
9.9
18.6
0.0
-0.5
2014
2015
2016
-1.4
-0.3
1.9
-1.5
0.5
2.3
-1.3
-5.8
-1.7
0.3
4.5
6.0
-1.9
0.8
2.2
-0.3
1.9
2.3
0.1
1.4
2.0
-0.5
3.9
4.0
-0.1
2.1
2.7
-1.1
-0.5
-1.4
2016 Q1
Q2
Q3
Q4
0.9
1.3
1.5
1.9
1.2
1.9
2.1
2.3
-2.1
-2.1
-2.9
-1.7
5.2
5.3
6.7
6.0
0.9
1.6
1.8
2.2
2.2
1.9
2.1
2.3
1.6
1.8
1.8
2.0
5.0
3.5
3.4
4.0
2.3
2.1
2.4
2.7
-0.4
-0.4
-0.9
-1.4
2016 Sep.
Oct.
Nov.
Dec.
1.5
1.7
1.8
1.9
2.1
2.2
2.1
2.3
-2.9
-1.1
-1.8
-1.7
6.7
5.6
6.6
6.0
1.8
1.8
1.9
2.2
2.1
1.9
2.1
2.3
1.8
1.8
1.9
2.0
3.4
3.7
3.6
4.0
2.4
2.2
2.5
2.7
-0.9
-1.1
-1.2
-1.4
2017 Jan.
Feb. (p)
1.8
1.5
2.3
2.0
-1.8
-2.1
5.5
4.0
2.1
2.3
2.4
2.4
2.2
2.3
4.6
4.1
2.8
2.9
-1.2
-1.3
Transactions
2014
2015
2016
2016 Q1
Q2
Q3
Q4
2016 Sep.
Oct.
Nov.
Dec.
Growth rates
Source: ECB.
1) Data refer to the changing composition of the euro area.
2) In accordance with the ESA 2010, in December 2014 holding companies of non-financial groups were reclassified from the non-financial corporations sector to the financial
corporations sector. These entities are included in MFI balance sheet statistics with financial corporations other than MFIs and insurance corporations and pension funds (ICPFs).
3) Including non-profit institutions serving households.
4) Adjusted for loan sales and securitisation (resulting in derecognition from the MFI statistical balance sheet) as well as for positions arising from notional cash pooling services
provided by MFIs.
ECB Economic Bulletin, Issue 3 / 2017 - Statistics
S 21
5 Money and credit
5.5 Counterparts to M3 other than credit to euro area residents 1)
(EUR billions and annual growth rates; seasonally adjusted; outstanding amounts and growth rates at end of period; transactions during period)
MFI liabilities
Central
government
holdings 2)
MFI assets
Longer-term financial liabilities vis-à-vis other euro area residents
Total
Deposits
Deposits
with an redeemable
agreed
at notice
maturity
of over
of over
3 months
2 years
1
2
3
2014
2015
2016
269.4
284.8
318.0
7,127.8
6,996.4
6,919.1
2,186.6
2,119.7
2,054.4
92.2
79.8
70.6
2016 Q1
Q2
Q3
Q4
314.7
319.3
310.1
318.0
6,962.3
7,006.3
6,960.6
6,919.1
2,113.6
2,094.1
2,068.5
2,054.4
2016 Sep.
Oct.
Nov.
Dec.
310.1
324.1
296.6
318.0
6,960.6
6,950.9
6,934.5
6,919.1
2017 Jan.
Feb. (p)
302.8
295.1
2014
2015
2016
2016 Q1
Q2
Q3
Q4
Net external
assets
Other
Debt
Capital
securities and reserves
with a
maturity
of over
2 years
4
5
Outstanding amounts
Total
Repos
with central
counterparties 3)
Reverse
repos to
central
counterparties 3)
6
7
8
9
10
2,388.1
2,254.0
2,140.8
2,460.8
2,543.0
2,653.3
1,381.1
1,343.8
1,131.5
217.8
264.9
238.4
184.5
205.9
205.9
139.7
135.6
121.6
76.9
74.6
72.4
70.6
2,179.5
2,175.8
2,125.1
2,140.8
2,592.3
2,661.8
2,694.6
2,653.3
1,293.8
1,292.4
1,196.1
1,131.5
293.1
296.8
284.6
238.4
247.1
238.0
209.2
205.9
152.1
144.0
129.1
121.6
2,068.5
2,071.2
2,061.6
2,054.4
72.4
72.4
71.9
70.6
2,125.1
2,123.5
2,136.6
2,140.8
2,694.6
2,683.9
2,664.4
2,653.3
1,196.1
1,138.4
1,108.9
1,131.5
284.6
295.1
294.7
238.4
209.2
193.0
194.7
205.9
129.1
133.7
121.3
121.6
6,875.2
6,921.5
2,037.8
2,026.3
69.8
69.6
2,127.8
2,129.3
2,639.8
2,696.3
1,110.9
1,101.4
225.5
265.5
176.5
171.4
106.3
104.4
-4.0
9.2
30.2
-165.5
-221.6
-148.1
-120.8
-106.2
-72.5
2.0
-13.5
-9.1
-154.5
-209.3
-120.6
107.8
107.3
54.1
237.7
-86.5
-282.6
-5.9
-15.1
-67.4
0.7
21.4
12.8
17.8
-4.0
-12.0
29.4
4.2
-9.2
5.8
-56.6
-13.0
-53.8
-24.8
-3.5
-22.3
-25.8
-20.8
-2.8
-1.8
-2.0
-2.6
-45.9
-15.9
-41.5
-17.3
-4.4
27.1
15.5
16.0
-74.8
-66.6
-98.2
-43.0
33.9
3.2
-12.2
-92.4
41.3
-9.2
-19.2
-0.2
17.3
-8.1
-13.7
-7.5
2016 Sep.
Oct.
Nov.
Dec.
-8.7
13.1
-27.6
20.3
-21.3
0.8
-10.3
-15.4
-9.4
-1.3
-11.7
-7.8
-0.6
-0.8
-0.5
-1.3
-15.8
-8.7
-5.4
-3.3
4.4
11.6
7.3
-2.9
-10.2
-53.2
-12.5
22.7
-17.2
-8.1
-28.6
-55.6
3.4
-13.1
1.7
11.2
-4.3
4.7
-12.4
0.3
2017 Jan.
Feb. (p)
-15.6
-8.2
-27.5
11.7
-10.3
-12.7
-0.8
-0.2
-5.5
-6.7
-10.8
31.4
2.6
-44.0
-52.1
49.6
-28.3
-5.1
-14.7
-2.0
2014
2015
2016
-1.6
3.6
10.6
-2.2
-3.1
-2.1
-5.1
-4.8
-3.4
2.2
-14.5
-11.5
-6.1
-8.6
-5.4
4.5
4.3
2.1
-
-
0.4
11.6
6.3
14.6
-2.9
-9.0
2016 Q1
Q2
Q3
Q4
11.0
20.1
5.3
10.6
-3.3
-2.3
-2.5
-2.1
-3.5
-2.9
-4.3
-3.4
-15.2
-13.3
-12.2
-11.5
-8.4
-6.8
-6.4
-5.4
2.0
2.8
2.7
2.1
-
-
3.8
3.6
1.5
6.3
-5.9
-2.9
-8.2
-9.0
2016 Sep.
Oct.
Nov.
Dec.
5.3
-7.2
0.1
10.6
-2.5
-2.1
-2.1
-2.1
-4.3
-3.4
-3.2
-3.4
-12.2
-11.8
-10.7
-11.5
-6.4
-6.0
-5.9
-5.4
2.7
2.8
2.5
2.1
-
-
1.5
4.5
-4.9
6.3
-8.2
-6.3
-15.6
-9.0
2017 Jan.
Feb. (p)
-1.4
-1.7
-2.1
-1.7
-3.5
-4.4
-11.3
-10.5
-4.8
-4.0
1.6
2.7
-
-
-12.2
-25.7
-23.8
-25.7
Transactions
Growth rates
Source: ECB.
1) Data refer to the changing composition of the euro area.
2) Comprises central government holdings of deposits with the MFI sector and of securities issued by the MFI sector.
3) Not adjusted for seasonal effects.
ECB Economic Bulletin, Issue 3 / 2017 - Statistics
S 22
6 Fiscal developments
6.1 Deficit/surplus
(as a percentage of GDP; flows during one-year period)
Deficit (-)/surplus (+)
Total
Central
government
State
government
Local
government
Memo item:
Primary
deficit (-)/
surplus (+)
Socual
security
funds
1
2
3
4
5
6
2013
2014
2015
2016
-3.0
-2.6
-2.1
-1.5
-2.6
-2.2
-1.9
-1.7
-0.2
-0.2
-0.2
-0.1
-0.1
0.0
0.1
0.2
-0.1
-0.2
-0.1
0.0
-0.2
0.1
0.3
0.7
2016 Q1
Q2
Q3
Q4
-1.9
-1.8
-1.8
-1.5
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
0.4
0.5
0.5
0.7
Sources: ECB for annual data; Eurostat for quarterly data.
6.2 Revenue and expenditure
(as a percentage of GDP; flows during one-year period)
Revenue
Total
Expenditure
Current revenue
Direct
taxes
Indirect
taxes
Capital
revenue
Total
Current expenditure
Net social
contributions
Compen- Intermediate
sation of consumption
employees
Capital
expenditure
Interest
Social
benefits
1
2
3
4
5
6
7
8
9
10
11
12
13
2013
2014
2015
2016
46.7
46.7
46.4
46.2
46.2
46.3
45.9
45.7
12.6
12.5
12.6
12.6
13.0
13.1
13.1
13.0
15.5
15.5
15.3
15.3
0.5
0.5
0.5
0.5
49.7
49.3
48.5
47.7
45.6
45.3
44.6
44.2
10.4
10.3
10.1
10.0
5.3
5.3
5.2
5.2
2.8
2.7
2.4
2.2
23.0
23.0
22.8
22.8
4.1
4.0
3.9
3.5
2016 Q1
Q2
Q3
Q4
46.4
46.3
46.3
46.3
45.9
45.8
45.8
45.8
12.6
12.5
12.6
12.6
13.1
13.1
13.1
13.0
15.3
15.4
15.4
15.4
0.5
0.5
0.5
0.5
48.3
48.1
48.1
47.8
44.5
44.3
44.3
44.3
10.1
10.0
10.0
10.0
5.2
5.2
5.2
5.2
2.3
2.3
2.2
2.2
22.8
22.8
22.8
22.9
3.9
3.8
3.8
3.5
Sources: ECB for annual data; Eurostat for quarterly data.
6.3 Government debt-to-GDP ratio
(as a percentage of GDP; outstanding amounts at end of period)
Total
Financial instrument
Holder
Original maturity
Currency Loans
Debt Resident creditors Non-resident
and
securities
creditors
deposits
MFIs
Up to
1 year
Over
1 year
Residual maturity
Currency
Up to
Over 1 Over
1 year and up to 5 years
5 years
Euro or
participating
currencies
Other
currencies
1
2
3
4
5
6
7
8
9
10
11
12
13
14
2013
2014
2015
2016
91.4
92.0
90.3
89.2
2.6
2.7
2.8
2.7
17.5
17.1
16.2
15.5
71.2
72.1
71.3
71.0
46.4
45.2
45.5
47.7
26.3
26.0
27.5
30.3
45.0
46.8
44.7
41.4
10.4
10.0
9.3
9.0
81.0
82.0
81.0
80.2
19.4
18.8
17.7
17.6
32.1
31.9
31.1
29.5
39.9
41.2
41.5
42.1
89.3
89.9
88.2
87.1
2.1
2.1
2.1
2.1
2016 Q1
Q2
Q3
Q4
91.3
91.2
90.1
89.3
2.7
2.7
2.7
2.7
16.2
16.0
15.6
15.5
72.4
72.5
71.7
71.1
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
Sources: ECB for annual data; Eurostat for quarterly data.
ECB Economic Bulletin, Issue 3 / 2017 - Statistics
S 23
6 Fiscal developments
6.4 Annual change in the government debt-to-GDP ratio and underlying factors 1)
(as a percentage of GDP; flows during one-year period)
Change in
debt-toGDP ratio 2)
Primary
deficit (+)/
surplus (-)
Deficit-debt adjustment
Total
Transactions in main financial assets
Total
Currency Loans
and
deposits
Debt
securities
Equity and
investment
fund shares
Revaluation
effects
and other
changes in
volume
Interestgrowth
differential
Other
Memo item:
Borrowing
requirement
1
2
3
4
5
6
7
8
9
10
11
12
2013
2014
2015
2016
1.9
0.6
-1.7
-1.1
0.2
-0.1
-0.3
-0.7
-0.2
-0.1
-0.9
-0.3
-0.8
-0.3
-0.5
0.2
-0.5
0.2
0.2
0.2
-0.4
-0.2
-0.2
-0.1
-0.2
-0.3
-0.3
0.0
0.4
0.0
-0.1
0.1
0.2
0.0
-0.1
-0.3
0.4
0.2
-0.3
-0.3
1.9
0.8
-0.5
-0.1
2.6
2.5
1.3
1.5
2016 Q1
Q2
Q3
Q4
-1.5
-0.9
-1.4
-1.1
-0.4
-0.5
-0.5
-0.7
-0.6
0.1
-0.5
-0.3
-0.2
0.4
-0.2
0.3
0.3
0.8
0.2
0.2
-0.2
-0.2
-0.1
-0.1
-0.3
-0.2
-0.3
0.0
0.0
0.0
0.0
0.1
0.0
-0.1
-0.2
-0.3
-0.4
-0.2
-0.1
-0.3
-0.5
-0.5
-0.4
-0.2
1.3
2.0
1.5
1.5
Sources: ECB for annual data; Eurostat for quarterly data.
1) Intergovernmental lending in the context of the financial crisis is consolidated except in quarterly data on the deficit-debt adjustment.
2) Calculated as the difference between the government debt-to-GDP ratios at the end of the reference period and a year earlier.
6.5 Government debt securities 1)
(debt service as a percentage of GDP; flows during debt service period; average nominal yields in percentages per annum)
Debt service due within 1 year 2)
Total
Principal
Average
residual
maturity
in years 3)
Interest
Maturities
of up to 3
months
Maturities
of up to 3
months
Average nominal yields 4)
Outstanding amounts
Total
Floating
Zero
rate coupon
Transactions
Fixed rate
Issuance Redemption
Maturities
of up to 1
year
1
2
3
4
5
6
7
8
9
10
11
12
13
2014
2015
2016
15.9
14.8
14.3
13.8
12.8
12.5
5.1
4.3
4.6
2.0
2.0
1.8
0.5
0.5
0.5
6.4
6.6
6.7
3.1
2.9
2.6
1.5
1.2
1.1
0.5
0.1
-0.1
3.5
3.3
3.0
2.7
3.0
2.9
0.8
0.4
0.2
1.6
1.2
1.2
2015 Q4
14.8
12.8
4.3
2.0
0.5
6.6
2.9
1.2
0.1
3.3
3.0
0.4
1.2
2016 Q1
Q2
Q3
15.1
15.0
14.5
13.2
13.1
12.7
4.7
4.8
4.0
1.9
1.8
1.8
0.5
0.5
0.5
6.6
6.7
6.8
2.8
2.7
2.6
1.2
1.1
1.2
0.0
-0.1
-0.1
3.2
3.1
3.1
2.8
2.9
2.8
0.3
0.3
0.2
1.1
1.1
1.2
2016 Oct.
Nov.
Dec.
14.5
14.6
14.3
12.7
12.8
12.5
3.8
4.3
4.6
1.8
1.8
1.8
0.5
0.5
0.5
6.9
6.9
6.9
2.6
2.6
2.6
1.1
1.1
1.1
-0.1
-0.1
-0.1
3.0
3.0
3.0
2.9
2.9
2.9
0.2
0.2
0.2
1.3
1.3
1.2
2017 Jan.
Feb.
Mar.
14.5
14.1
14.4
12.7
12.4
12.6
4.9
4.2
4.3
1.8
1.7
1.7
0.5
0.4
0.4
6.9
7.0
6.9
2.6
2.6
2.6
1.1
1.1
1.1
-0.1
-0.2
-0.1
3.0
3.0
3.0
2.9
2.9
2.9
0.2
0.2
0.2
1.2
1.3
1.1
Source: ECB.
1) At face value and not consolidated within the general government sector.
2) Excludes future payments on debt securities not yet outstanding and early redemptions.
3) Residual maturity at the end of the period.
4) Outstanding amounts at the end of the period; transactions as 12-month average.
ECB Economic Bulletin, Issue 3 / 2017 - Statistics
S 24
6 Fiscal developments
6.6 Fiscal developments in euro area countries
(as a percentage of GDP; flows during one-year period and outstanding amounts at end of period)
Belgium
Germany
Estonia
Ireland
1
2
3
2013
2014
2015
2016
-3.1
-3.1
-2.5
-2.6
-0.2
0.3
0.7
0.8
-0.2
0.7
0.1
0.3
-5.7
-3.7
-2.0
-0.6
2016 Q1
Q2
Q3
Q4
-2.6
-2.6
-3.0
-2.6
0.8
0.8
0.6
0.8
0.7
0.8
0.5
0.3
-1.6
-1.6
-1.8
-0.6
2013
2014
2015
2016
105.6
106.7
106.0
105.9
77.5
74.9
71.2
68.3
10.2
10.7
10.1
9.5
2016 Q1
Q2
Q3
Q4
109.2
109.7
108.7
105.9
70.9
70.2
69.5
68.3
9.9
9.7
9.6
9.5
Greece
Spain
France
Italy
Cyprus
6
7
8
9
-13.1
-3.7
-5.9
0.7
-7.0
-6.0
-5.1
-4.5
-4.0
-3.9
-3.6
-3.4
-2.9
-3.0
-2.7
-2.4
-5.1
-8.8
-1.2
0.4
-4.8
-3.7
-1.8
0.7
-5.1
-5.3
-4.8
-4.5
-3.5
-3.3
-3.4
-3.4
-2.6
-2.4
-2.4
-2.4
-0.3
-1.3
-1.0
0.4
119.5
105.3
78.7
75.4
177.4
179.7
177.4
179.0
95.5
100.4
99.8
99.4
92.3
94.9
95.6
96.0
129.0
131.8
132.1
132.6
102.2
107.1
107.5
107.8
80.1
77.7
77.1
75.4
176.4
179.7
176.3
179.0
101.2
101.1
100.4
99.4
97.6
98.4
97.5
96.6
134.8
135.4
132.7
132.6
108.4
107.5
110.6
107.8
4
5
Government deficit (-)/surplus (+)
Government debt
Latvia
Lithuania Luxembourg
Malta
Netherlands
Austria
Portugal
Slovenia
Slovakia
Finland
13
14
15
Government deficit (-)/surplus (+)
16
17
18
19
10
11
12
2013
2014
2015
2016
-1.0
-1.6
-1.3
0.0
-2.6
-0.7
-0.2
0.3
1.0
1.4
1.4
1.6
-2.6
-2.0
-1.3
1.0
-2.4
-2.3
-2.1
0.4
-1.4
-2.7
-1.1
-1.6
-4.8
-7.2
-4.4
-2.0
-15.1
-5.4
-2.9
-1.8
-2.7
-2.7
-2.7
-1.7
-2.6
-3.2
-2.7
-1.9
2016 Q1
Q2
Q3
Q4
-0.7
-0.4
0.2
0.0
-0.1
0.4
0.2
0.3
1.3
1.1
1.1
1.6
-0.3
0.4
0.8
1.0
-1.9
-1.0
-0.4
0.4
-1.0
-0.9
-0.6
-1.6
-3.7
-3.5
-3.7
-2.0
-2.7
-1.8
-1.7
-1.8
-2.5
-2.3
-2.0
-1.7
-2.4
-2.4
-2.2
-1.9
2013
2014
2015
2016
39.0
40.9
36.5
40.1
38.7
40.5
42.7
40.2
23.4
22.4
21.6
20.0
68.7
64.3
60.6
58.3
67.7
67.9
65.2
62.3
81.3
84.4
85.5
84.6
129.0
130.6
129.0
130.4
71.0
80.9
83.1
79.7
54.7
53.6
52.5
51.9
56.5
60.2
63.7
63.6
2016 Q1
Q2
Q3
Q4
36.3
38.9
37.9
40.1
40.0
40.1
41.3
40.2
21.9
21.4
20.9
20.0
61.8
61.0
59.7
58.3
64.9
63.8
62.0
62.3
86.5
86.2
83.7
84.6
128.9
131.6
133.1
130.4
83.6
82.5
82.8
79.7
51.8
52.9
52.7
51.9
64.3
61.9
61.8
63.6
Government debt
Source: Eurostat.
ECB Economic Bulletin, Issue 3 / 2017 - Statistics
S 25
© European Central Bank, 2017
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All rights reserved. Reproduction for educational and non-commercial purposes is permitted provided that the source is acknowledged.
This Bulletin was produced under the responsibility of the Executive Board of the ECB. Translations are prepared and published by the
national central banks.
The cut-off date for the statistics included in this issue was 26 April 2017.
ISSN
ISSN
EU catalogue No
EU catalogue No
2363-3417 (html)
2363-3417 (pdf)
QB-BP-17-003-EN-Q (html)
QB-BP-17-003-EN-N (pdf)
DOI
DOI
10.2866/165871 (html)
10.2866/165871 (pdf)